Localizing seizure onset zones in surgical epilepsy with neurostimulation deep learning

Abstract

This was the first demonstration, to the authors' knowledge, that a deep learning framework can be used to accurately classify SOZs with single-pulse electrical stimulation-evoked responses. These findings suggest that accurate classification of SOZs relies on a complex temporal evolution of evoked responses within 350 msec of stimulation. Validation in a larger data set could provide a practical clinical tool for the presurgical evaluation of drug-resistant epilepsy.