Cited 0 times in
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 강훈철 | - |
dc.date.accessioned | 2023-03-10T01:34:22Z | - |
dc.date.available | 2023-03-10T01:34:22Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/193234 | - |
dc.description.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. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | PMLR | - |
dc.relation.isPartOf | Proceedings of Machine Learning Research | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Pediatrics (소아과학교실) | - |
dc.contributor.googleauthor | Kwanhyung Lee | - |
dc.contributor.googleauthor | Hyewon Jeong | - |
dc.contributor.googleauthor | Seyun Kim | - |
dc.contributor.googleauthor | Donghwa Yang | - |
dc.contributor.googleauthor | Hoon-Chul Kang | - |
dc.contributor.googleauthor | Edward Choi | - |
dc.identifier.doi | 10.48550/arXiv.2201.08780 | - |
dc.contributor.localId | A00102 | - |
dc.relation.journalcode | J04389 | - |
dc.identifier.eissn | 2640-3498 | - |
dc.contributor.alternativeName | Kang, Hoon Chul | - |
dc.contributor.affiliatedAuthor | 강훈철 | - |
dc.citation.volume | 174 | - |
dc.citation.startPage | 311 | - |
dc.citation.endPage | 337 | - |
dc.identifier.bibliographicCitation | Proceedings of Machine Learning Research, Vol.174 : 311-337, 2022-01 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.