TY - JOUR
T1 - Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks
AU - Craley, Jeff
AU - Jouny, Christophe
AU - Johnson, Emily
AU - Hsu, David
AU - Ahmed, Raheel
AU - Venkataraman, Archana
N1 - Publisher Copyright:
© 2022 Craley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/2
Y1 - 2022/2
N2 - We propose a novel neural network architecture, SZTrack, to detect and track the spatiotemporal 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 the crosssite generalization capabilities of SZTrack on a dataset of 53 seizure recordings from 14 epilepsy patients acquired at the University of Wisconsin Madison. SZTrack is able to determine the lobe and hemisphere of origin in nearly all of these new patients without retraining the network. To our knowledge, SZTrack is the first end-to-end seizure tracking network using scalp EEG.
AB - We propose a novel neural network architecture, SZTrack, to detect and track the spatiotemporal 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 the crosssite generalization capabilities of SZTrack on a dataset of 53 seizure recordings from 14 epilepsy patients acquired at the University of Wisconsin Madison. SZTrack is able to determine the lobe and hemisphere of origin in nearly all of these new patients without retraining the network. To our knowledge, SZTrack is the first end-to-end seizure tracking network using scalp EEG.
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U2 - 10.1371/journal.pone.0264537
DO - 10.1371/journal.pone.0264537
M3 - Article
C2 - 35226686
AN - SCOPUS:85125600131
SN - 1932-6203
VL - 17
JO - PloS one
JF - PloS one
IS - 2 February
M1 - e0264537
ER -