Objective:

To develop and validate a deep learning model to predict time-to-seizure in patients with epilepsy (PWE) from routine EEG.

Background:

While interictal epileptiform discharges (IEDs) on EEG are associated with higher seizure recurrence, routine EEG has low sensitivity for IEDs and is prone to overinterpretation. Deep learning can extract features from EEG beyond IEDs and map them to complex outcomes, such as seizure risk through time, offering valuable information to guide epilepsy management.

Design/Methods:

We selected all PWE undergoing routine EEG at our institution from 2018–2019, using EEGs recorded after July 2019 as the testing set. Patients with unclear epilepsy diagnoses or seizure during the EEG were excluded. Medical charts were reviewed for the date of the first seizure after the EEG (exact date or extrapolated from seizure frequency) and the date of last follow-up. EEGs were segmented into overlapping 30-second windows and input into a deep transformer model alongside the following clinical features: age, sex, epilepsy type, epilepsy duration, seizure frequency prior to EEG, focal lesion on neuroimaging, family history of epilepsy, and history of febrile seizures. A random survival forest (RSF) using clinical features only was used as a baseline. Models were trained to predict seizure hazards over 18 months at logarithmically spaced intervals and evaluated on the testing set using Uno’s concordance index.

Results:

We included 504 EEGs from 451 patients for training and 92 EEGs from 83 patients for testing. The deep learning model achieved a concordance index of 0.67, compared to 0.63 for the clinical-only RSF model. Including IEDs as a predictor did not improve the RSF model’s performance.

Conclusions:

Deep learning can extract complex information from routine EEG to predict time-to-seizure, outperforming traditional predictors. This suggests a potential role of automated EEG analysis in the follow-up of PWE.