Improving diagnostic accuracy of routine EEG for epilepsy using deep learning.

ABSTRACT: The yield of routine EEG to diagnose epilepsy is limited by low sensitivity and the potential for misinterpretation of interictal epileptiform discharges. Our objective is to develop, train and validate a deep learning model that can identify epilepsy from routine EEG recordings, complementing traditional interpretation based on identifying interictal discharges. This is a retrospective cohort study of diagnostic accuracy. All consecutive patients undergoing routine EEG at our tertiary care centre between January 2018 and September 2019 were included. EEGs recorded between July 2019 and September 2019 constituted a temporally shifted testing cohort. The diagnosis of epilepsy was established by the treating neurologist at the end of the available follow-up period, based on clinical file rev

SUBMITTER: Lemoine E 

PROVIDER: S-EPMC12419360 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature