Seizure Onset Zone Localization in Drug-Resistant Epilepsy Using Self-Supervised Learning on Stereo-EEG

Accurate localization of the seizure onset zone (SOZ) is a central determinant of surgical outcome in drug-resistant focal epilepsy, yet identifying it from stereo-electroencephalography (SEEG) remains a slow, subjective visual task. We developed a self-supervised CNN--Transformer encoder (CSOPE-Net; Contrastive Seizure-Onset Pattern Encoder) that learns contact-level peri-ictal representations from 60-second superlet spectrograms through InfoNCE contrastive pretraining. We evaluated this representation as a framework for SOZ localization, seizure-onset phenotype clustering, and identification of clinically labeled non-SOZ contacts with SOZ-like morphology in poor-outcome patients. Across 149 patients partitioned a priori into a development cohort (n=119) and an independent held-out cohort (n=30; 18 good-outcome subjects for classification validation and 12 poor-outcome subjects for SOZ-proximal replication), the model achieved aggregate ROC-AUC 0.854 under leave-one-subject-out cross-validation, 0.935 on held-out good-outcome subjects, and 0.822 on an independent external cohort (HUP iEEG dataset), with consistent performance across patients. The learned representation organized seizure onsets into reproducible phenotype families and, in poor-outcome patients, flagged clinically labeled non-SOZ contacts whose spectrotemporal features resembled those of high-confidence SOZ contacts. This signal reproduced in held-out data, and in a blinded re-review three experts endorsed these contacts as showing ictal-onset morphology at approximately 15-fold higher odds than matched non-SOZ controls. This framework augments expert SEEG review and surfaces candidate contacts for re-review in poor-outcome cases.