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Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting

Authors
 Kwanhyung Lee  ;  Hyewon Jeong  ;  Seyun Kim  ;  Donghwa Yang  ;  Hoon-Chul Kang  ;  Edward Choi 
Citation
 Proceedings of Machine Learning Research, Vol.174 : 311-337, 2022-01 
Journal Title
Proceedings of Machine Learning Research
Issue Date
2022-01
Abstract
Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG sig nals with modern deep learning models to re duce the linical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings.
Also, some of them are not trained in real-time seizure detection tasks, making it hard for on device applications. In this work, for the first time, we extensively compare multiple state-of the-art models and signal feature extractors in a real-time seizure detection framework suitable for real-world application, using various evalu ation metrics including a new one we propose to evaluate more practical aspects of seizure de tection models.
Files in This Item:
T202300817.pdf Download
DOI
10.48550/arXiv.2201.08780
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pediatrics (소아과학교실) > 1. Journal Papers
Yonsei Authors
Kang, Hoon Chul(강훈철) ORCID logo https://orcid.org/0000-0002-3659-8847
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/193234
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