Automatic Electrophysiological Seizure Patterns Identification in Long-Term Ambulatory Intracranial Recordings via a Deep Learning Model

VICTORIA PETERSON; VASILEIOS KOKKINOS; ENZO FERRANTE; ASHLEY WALTON; AMIR HADANNY; VARUN SARAVANAN; NATHANIEL SISTERSON1; NAOIR ZAHER; ALEXANDRA URBAN; RICHARDSON, R. MARK

Conferencia; The American Epilepsy Society 2022 Annual Meeting; 2022

Managing the progress of drug-resistant epilepsy patients implanted with the responsive neurostimulation (RNS) system requires the manual evaluation of hundreds of hours of brief epochs of continuous intracranial recordings1. The generation of these large amounts of valuable data and the scarcity of experts’ time for evaluation, necessitates the development of automatic tools to detect intracranial electroencephalographic seizure patterns (iESPs) with expert-level accuracy. We developed an deep learning system for identifying the presence and onset time of iESPs in intracranial EEG (iEEG) recordings from the RNS device.