Seizure detection from EEG data

This repo contains the main training script used in our work "Towards trustworthy seizure onset detection using workflow notes" [arXiv].

Requirements

pytorch, torchvision, torchaudio, pytorch-lightning, transformers, hydra-core, rich, wandb, hydra_colorlog (pip), eeghdf=0.1 (pip), einops, opt_einsum, scipy (pip, for s4 model)

Training run

Configurations for training runs are in the configs/experiment directory. To train the binary seizure onset detection model, run python train.py experiment=eeg_classification.

Codebase Structure

Notation
├── folders
|=== files

    |=== train.py                              # (MAIN) running the model
    ├── configs                    
        |=== config.yaml                       # default config file 
    ├── src
        ├── models                             # contains model architectures
        ├── callbacks                          
        ├── optim                           
        ├── tasks                           
        ├── datamodules                        # directory for datamodules                 
            |=== eeg_datamodule.py             # defines EEG dataloaders
            |=== eeg_utils.py                  # utility functions for EEG data
    ├── datasets                               # directory where datasets live
    └── README.md

Acknowledgements

Code copied, adapted and inspired from the following repositories:

• https://github.com/HazyResearch/zoo
• https://github.com/HazyResearch/state-spaces
• https://github.com/tsy935/eeg-gnn-ssl

Citation

If you use this codebase, or otherwise found our work valuable, please cite:

@inproceedings{saab2023towards,
  title={Towards Trustworthy Seizure Onset Detection Using Workflow Notes},
  author={Saab, Khaled and Tang, Siyi, and Taha, Mohamed, and Lee-Messer, Christopher, and R{\'e}, Christopher, and Rubin, Daniel},
  year={2023},
}