Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework.

Delineating the epileptogenic zone (EZ) is essential for achieving seizure freedom in drug-resistant epilepsy (DRE). Conventionally, seizure onset derived from ictal intracranial EEG (iEEG) approximates the EZ, but acquiring ictal data can be challenging. Interictal iEEG abnormalities offer abundant, non-seizure-dependent markers of the epileptogenic tissue; however, these biomarkers offer limited specificity. Here, we propose a machine learning framework that integrates interictal spike and ripple features to predict the epileptogenic contacts targeted for surgical removal and the patient’s surgical outcome. We retrospectively analyzed iEEG data from 62 children with DRE [34 with good outcome (Engel I)], automatically detected spikes and ripples, and computed temporal, spectral, and spatial features for each channel. Using combinations of these features and the resected contacts as targets, we trained Random Forest classifiers using only good outcome patients to estimate epileptogenic contacts. Spike-based and combined spike and ripple features outperformed individual biomarkers in predicting the epileptogenic contacts with an area under the receiver operating characteristic curve of 0.89 and 74% spatial overlap with resection. Although most individual features and classifiers predicted outcome, the combined feature model performed best (i.e., sensitivity 88%, specificity 68%, and accuracy 79%). Our findings demonstrate that integrating multimodal interictal features improves the identification of epileptogenic contacts providing valuable prognostic insights for epilepsy surgery.