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, easily acquired, 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 automatically delineate the EZ and predict outcome with improved performance compared to individual biomarkers. We retrospectively analyzed iEEG data from 62 children with DRE (34 good (Engel 1) outcomes undergoing neurosurgery, automatically detected spikes and ripples, and computed temporal, spectral, and spatial features for each channel. We trained Random Forest classifiers to predict the EZ using combinations of these features. The predicted EZ derived from spike-based and combined spike-ripple feature sets outperformed those from individual biomarkers in defining the EZ, with an area under the receiver operating characteristic curve of 0.9 and 74% spatial overlap with resection. Although most individual features and classifiers predicted the 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 EZ delineation, providing valuable prognostic insights for epilepsy surgery.