Development and validation of a deep survival model to predict time to seizure from routine electroencephalography
EEGSurvNet demonstrates that deep learning can extract prognostic information from routine EEG beyond visible epileptiform abnormalities, potentially improving patient counseling and treatment decisions. Future prospective studies are needed to validate these findings and assess their clinical impact.
Authors
- Émile Lemoine (ORCID: https://orcid.org/0000-0002-8977-3186)
- Frédéric J. Lesage (ORCID: https://orcid.org/0000-0001-8241-7712)
- Mezen Jemel
- Elie Bou Assi
- An Qi Xu
- Dang K Nguyen
Institutions
- Centre Hospitalier de l’Université de Montréal (CA)
- Polytechnique Montréal (CA)
- Université de Montréal (CA)
Publication Details
- Journal
- Epilepsia
- Published
- 2026-01-19
- DOI
- https://doi.org/10.1002/epi.70101
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00