seizyml 2.0.0

A CLI tool for seizure detection using interpretable ML

• SeizyML uses interpretable machine learning models to detect 🕵️‍♂️ seizures from EEG recordings coupled with manual verification in user-friendly GUI.
• 📖 To reference SeizyML, or view the manuscript, please refer to the following publication (To be updated soon).
• You can access the data and code used to reproduce the experiments and figures from the accompanying paper on Zenodo.

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📚 Contents

• ⚙️ Hardware Requirements
• 💾 Installation
• 📋 How To Use
• 🛠️ App Configuration

📄 Additional Resources

• Configuration settings
• SeizyML Processing Pipeline
• Custom Model Integration

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Hardware requirements

• SeizyML is a lightweight application that utilizes Gaussian Naive Bayes (GNB) models to predict seizure events from EEG data.
• Any modern CPU with sufficient RAM to load your EEG recordings should work effectively.
• For example, a quad-core CPU with 16 GB RAM can efficiently handle 24-hour long EEG recordings with 2 channels sampled at 4000 Hz.
• No GPU is required for SeizyML's operation.

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Installation

Conda (Recommended)

1. Download and install miniconda.

2. Clone or Download SeizyML on your machine.

3. Start Anaconda's prompt, navigate to the downloaded /seizy_ml to create the conda environment:

    conda env create -f environment.yml
   

4. Activate environment

    conda activate seizyml
   

5. Launch App

    seizyml
   

Pip

1. Download and install Python 3.9.

2. In the terminal

    pip install seizyml
   

3. Launch App

    seizyml
   

If this works you should see the SeizyMl CLI interface.

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How To Use

🚀 Quick Start Guide

# 1. Activate environment
conda activate seizyml

# 2. Train the model
seizyml train-model

# 3. Set data path
seizyml set-datapath /path/to/data

# 4. Preprocess EEG data
seizyml preprocess

# 5. Generate predictions
seizyml predict

# 6. Verify predictions via GUI
seizyml verify

# Repeat step 6 until all recordings are verified.

# 7. Extract seizure properties
seizyml extract-properties

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📄 Detailed instructions

1. Launch App.

For conda:

# In anaconda prompt
conda activate seizyml
seizyml

For pip:

# In terminal
seizyml

2. Train Model (Skip to step 3 if a model was trained).

seizyml train-model

• You will be prompted to enter the full path to the training directory.
• This is the folder path where the training data in .h5 format along with the corresponding training labels in .csv format are stored.
• The training data consist of each recording in .h5 format [Nsegments, 1 segment, Nchannels]. Where a segment is 500 (win*fs).
• For data conversion check the accompanying app seizy_convert or the h5_conversion script for more help.
• The training labels consist of a corresponding .csv file containing the ground truth labels (1 for seizure, 0 for non seizure) with length [Nsegments].
• Training data and labels for each recording need to have a matching name.

• After this a GUI will be launched to allow editing the settings.
• The only field that requires editing (given default formatting) is the channels field.

•     **channels** : List containing the names of LFP/EEG channels, e.g. ["hippocampus", "frontal cortex"]
  

3. Set DataPath.

seizyml set-datapath <data_path>

• The first is the full path to the parent directory where the child folder with h5 data resides and where all subsequent folders will be created. Check configuration settings for more information.
• The h5 data should be added in a child folder called data.

/parent_directory/
└── data/
    ├── file1.h5
    └── file2.h5

• The data need to be in the same format as the data used to train the model.

4. Preprocess data.

seizyml preprocess

• This is the step where the h5 data files will be filtered and large outliers will be removed.

5. Generate model prections.

seizyml predict

• Here selected features will be extracted and model predictions will be generated using the selected model (model id can be found in the configuration settings file).
• For more informtaion check the model pipeline

6. Verify seizures and adjust seizure boundaries.

seizyml verify

• This will launch a prompt to allow for file selection for verification.
• After file selection, a GUI will be launched for seizure verification and boundary adjustment.
• Repeat this command until all files are verified.
• 📄 Note: if you choose to re-verify a file, the app will remember which seizures where accepted/rejected/not-verified but not the refined seizure boundaries.

7. Get seizure properties.

seizyml extract-properties

• After all files are verified run this command to get seizure properties
• This step will generate a csv file with seizure properties for each h5 file.
• Current properties extracted per file are:

'seizure_number'
'avg_seizure_dur_sec'
'total_time_seizing_sec'
'coefficient_of_variation'
'recording_dur_hrs

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Other important functions

1. Select Model.

seizyml select-model <model_path> <user_settings_path>

• This function allows the user to select a model
• Before using SeizyML for seizure detection a model should be first trained on ground truth (hand-scored) data.

2. Feature Contributions

seizyml feature-contribution

• Features contribution to the GNB model can be visualized using the following command.

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App Configuration

All settings are stored in the user_settings.yaml file.

• This file will be created in the training folder specified by the user when they run seizyml train-model command.
• To edit the user_settings.yaml use any text editor such as notepad:
• An explanation of all other settings can be found here.

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🤝 Contributions

We welcome all project contributions including raising issues and pull requests!

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📬 Support

If you encounter issues, please submit them via the GitHub issue tracker.

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