Development and Validation of a Deep Survival Model to Predict Time-to-Seizure from Routine EEG
Bibliographic record
Abstract
Abstract Objective To develop and validate a deep survival model (EEGSurvNet) that analyzes routine EEG to predict individual seizure risk over time, comparing its performance to traditional clinical predictors such as interictal epileptiform discharges (IEDs). Methods We conducted a retrospective cohort study including 1,014 consecutive routine EEGs from 994 patients recorded at a tertiary epilepsy center. We developed EEGSurvNet, a deep learning model that predicts time-to-next-seizure over a two-year horizon from a single EEG. Model performance was evaluated on a temporally-shifted testing set of 135 EEGs from 115 patients using time-dependent area under the receiver operating characteristic curve (AUROC), AUROC integrated over two years (iAUROC), and C-index. We compared the deep survival model to a clinical Cox model incorporating standard risk factors as well as a random model based on baseline seizure risk. Results EEGSurvNet achieved a two-year iAUROC of 0.69 (95%CI: 0.64–0.73) and C-index of 0.66 (0.60–0.73), outperforming both clinical and random models. Performance was highest in the first months following EEG, peaking at 2 months (AUROC = 0.80). Combining EEGSurvNet to clinical predictors further improved performances (iAUROC = 0.70, C = 0.69). Notably, the model showed superior discrimination on EEGs without IEDs (iAUROC = 0.78 vs 0.53). Model interpretation revealed that the temporal-occipital regions and 6–15 Hz frequencies contributed most to risk prediction. Significance 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. Key Points Deep learning model predicts individual seizure risk from routine EEG over 2 years Model performs better on EEGs without epileptiform discharges, suggesting novel biomarkers Temporal-occipital regions and 6-15 Hz frequencies contribute most to risk prediction Combined clinical-EEG model achieves best performance for seizure risk stratification
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".