Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks

ABSTRACT: We propose a novel neural network architecture, SZTrack, to detect and track the spatio-temporal propagation of seizure activity in multichannel EEG. SZTrack combines a convolutional neural network encoder operating on individual EEG channels with recurrent neural networks to capture the evolution of seizure activity. Our unique training strategy aggregates individual electrode level predictions for patient-level seizure detection and localization. We evaluate SZTrack on a clinical EEG dataset of 201 seizure recordings from 34 epilepsy patients acquired at the Johns Hopkins Hospital. Our network achieves similar seizure detection performance to state-of-the-art methods and provides valuable localization information that has not previously been demonstrated in the literature. We also show t

SUBMITTER: Craley J 

PROVIDER: S-EPMC8884583 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature