DREChanNet: Patient-Relative iEEG Model for Channel-Wise Epileptogenic Zone Localization

Abstract

Accurate localization of the epileptogenic zone (EZ) is essential for presurgical evaluation of patients with drug-resistant focal epilepsy. Intracranial electroencephalography (iEEG) provides direct recordings of seizure-related brain activity, yet identifying epileptogenic contacts remains challenging because background electrophysiological activity and electrode coverage vary substantially across patients. These differences complicate the interpretation of absolute signal measurements and highlight the need to account for patient-specific recording context. We propose a patient-relative (PR) plug-in that expresses physiological features or learned representations relative to the distribution of recorded channels within each patient. This label-free operation can be incorporated as an input transformation or a lightweight correction to an existing predictor. We further introduce DREChanNet, a compact ictal localization model integrating six physiological descriptors with learned raw-signal representations. Same-channel pre-onset contrasts characterize seizure-related changes, cross-seizure aggregation preserves response magnitude and variability, and PR contextualizes both branches before prediction. We investigate within-patient physiological associations and held-out-channel reference prediction, followed by baseline comparisons, component ablations, cross-source transfer, seizure-sufficiency analysis, and channel ranking. Evaluation includes an ictal cohort of 80 patients and a patient-based interictal cohort of 124 patients. PR improves macro-F1 by an average of 9.03 percentage points across eight ictal baselines and improves the separately fitted interictal readout. These findings support patient-relative contextualization as a reusable approach to channel-level localization and establish DREChanNet as an integrated model for ictal iEEG analysis.