Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Seizure detection from multi-channel EEG using entropy-based dynamic graph embedding

Authors
Li, G.Jung, Jason J.
Issue Date
Dec-2021
Publisher
Elsevier B.V.
Keywords
Dynamic graph embedding; Graph entropy; Seizure detection
Citation
Artificial Intelligence in Medicine, v.122
Journal Title
Artificial Intelligence in Medicine
Volume
122
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/51656
DOI
10.1016/j.artmed.2021.102201
ISSN
0933-3657
1873-2860
Abstract
An epileptic seizure is a chronic disease with sudden abnormal discharge of brain neurons, which leads to transient brain dysfunction. To detect epileptic seizures, we propose a novel idea based on a dynamic graph embedding model. The dynamic graph is built by identifying the correlation among the multi-channel EEG signals. Graph entropy measurement is exploited to calculate the similarity among the graph at each time interval and construct the graph embedding space. Since the abnormal electrical brain activity causes the epileptic seizure, the graph entropy during the seizure time interval is different from other time intervals. Therefore, we propose an entropy-based dynamic graph embedding model to cluster the graphs, and the graphs with epileptic seizures are discriminated. We applied the proposed approach to the Children Hospital Boston-Massachusetts Institute of Technology Scalp EEG database. The results have shown that the proposed approach outperformed the baselines by 1.4% with respect to accuracy. © 2021 Elsevier B.V.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Software > School of Computer Science and Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Jung, Jason J. photo

Jung, Jason J.
소프트웨어대학 (소프트웨어학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE