Unsupervised automatic seizure detection for focal-onset seizures recorded with behind-the-ear EEG using an anomaly-detecting generative adversarial network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | You, Sungmin | - |
dc.contributor.author | Cho, Baek Hwan | - |
dc.contributor.author | Yook, Soonhyun | - |
dc.contributor.author | Kim, Joo Young | - |
dc.contributor.author | Shon, Young-Min | - |
dc.contributor.author | Seo, Dae-Won | - |
dc.contributor.author | Kim, In Young | - |
dc.date.accessioned | 2022-07-07T15:02:57Z | - |
dc.date.available | 2022-07-07T15:02:57Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2020-09 | - |
dc.identifier.issn | 0169-2607 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145149 | - |
dc.description.abstract | Background and objective: Epilepsy is a neurological disorder of the brain, which involves recurrent seizures. An encephalogram (EEG) is a gold standard method in the detection and analysis of epileptic seizures. However, the standard EEG recording system is too obstructive to be used in daily life. Behind-the-ear EEG is an alternative approach to record EEG conveniently. Previous researchers applied machine learning to automatically detect seizures with EEG, but the epileptic EEG waveform contains subtle changes that are difficult to be identified. Furthermore, the extremely small proportion of ictal events in the long-term monitoring may cause the imbalance problem and, consequently, poor prediction performance in supervised learning approaches. In this study, we present an automatic seizure detection algorithm with a generative adversarial network (GAN) trained by unsupervised learning and evaluated it with behind-the-ear EEG. Methods: We recorded behind-the-ear EEGs from 12 patients who have various types of epilepsy. Data were reviewed separately by two epileptologists, who determined the onsets and ends of seizures. First, we conducted unsupervised learning with the normal records for the GAN to learn the representation of normal states. Second, we performed automatic seizure detection with the trained GAN as an anomaly detector. Last, we combined the Gram matrix with other anomaly losses to improve detection performance. Results: The proposed approach achieved detection performance with an area under the receiver operating curve of 0.939 and sensitivity of 96.3% with a false alarm rate of 0.14 per hour in the test dataset. In addition, we confirmed distinguishability with the distribution of the anomaly scores in terms of EEG frequency bands. Conclusions: It is expected that the proposed anomaly detection via GAN with the behind-the-ear EEG can be effectively used for long-term seizure monitoring in daily life. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER IRELAND LTD | - |
dc.title | Unsupervised automatic seizure detection for focal-onset seizures recorded with behind-the-ear EEG using an anomaly-detecting generative adversarial network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, In Young | - |
dc.identifier.doi | 10.1016/j.cmpb.2020.105472 | - |
dc.identifier.scopusid | 2-s2.0-85083728151 | - |
dc.identifier.wosid | 000548661400008 | - |
dc.identifier.bibliographicCitation | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v.193, pp.1 - 8 | - |
dc.relation.isPartOf | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE | - |
dc.citation.title | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE | - |
dc.citation.volume | 193 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 8 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.subject.keywordPlus | EPILEPSY | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Electroencephalography | - |
dc.subject.keywordAuthor | Epilepsy | - |
dc.subject.keywordAuthor | Seizures | - |
dc.subject.keywordAuthor | Generative adversarial network | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0169260719320000?via%3Dihub | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.