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language: en
license: mit
tags:
- eeg
- epilepsy
- medical
- vision-transformer
- deep-learning
- survival
- deepsurv
datasets:
- private
metrics:
- c-index
- auroc
library_name: pytorch
---
# EEGSurvNet: A Deep Survival Model to Predict Time-to-Seizure After EEG
## Model Description
EEGSurvNet predicts the long-term risk of seizure recurrence through time after routine EEG. The input is a 60s EEG segment with 19 channels in spectrogram format (see below for specifications). The output is the logit of the seizure recurrence hazards at 6 discrete time intervals.
**Paper**: [Development and validation of a deep survival model to predict time to seizure from routine electroencephalography](https://onlinelibrary.wiley.com/doi/10.1002/epi.70101?af=R) (Epilepsia, 2026)
**Repository**: [GitLab Repository]() - See README for full documentation and preprocessing code
## Usage - WIP
**Note**: See the [GitLab repository README](https://gitlab.com/chum-epilepsy/epi_surv/README.md) for complete preprocessing pipeline, BIDS format requirements, and dataloader examples.
## Training Data
- 1,014 routine EEG recordings from 994 patients
- Tertiary care center (CHUM, Montreal)
- Temporal split: Training/validation (Jan 2018 - Sep 2019), Testing (Sep-Dec 2019)
- Median follow-up: 2.2 years (training)
## Performance
- 2-year integrated AUROC: 0.69 (95% CI: 0.64–0.73)
- C-index: 0.66 (0.60-0.73)
- Maximal AUROC at 2 months (0.80)
- Outperforms spike-based predictions
## Citation
```bibtex
@article{https://doi.org/10.1002/epi.70101,
author = {Lemoine, Émile and Xu, An Qi and Jemel, Mezen and Lesage, Frédéric and Nguyen, Dang K. and Bou Assi, Elie},
title = {Development and validation of a deep survival model to predict time to seizure from routine electroencephalography},
journal = {Epilepsia},
volume = {n/a},
number = {n/a},
pages = {},
keywords = {artificial intelligence, biomarkers, electroencephalography, prognosis, survival analysis},
doi = {https://doi.org/10.1002/epi.70101},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/epi.70101},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/epi.70101},
}
```
## Authors
Émile Lemoine, An Qi Xu, Mezen Jemel, Frédéric Lesage, Dang Khoa Nguyen, Elie Bou Assi
## Contact
For questions, please contact the corresponding author or submit an issue on the GitLab repository.
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