| | --- |
| | 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 |
| |
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| |  |
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|
| | ## Model Description |
| |
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| | 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. |
| |
|
