Contextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Woonghee | - |
dc.contributor.author | Lee, Jaeyoung | - |
dc.contributor.author | Kim, Younghoon | - |
dc.date.accessioned | 2023-08-16T07:42:51Z | - |
dc.date.available | 2023-08-16T07:42:51Z | - |
dc.date.issued | 2021-11 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114138 | - |
dc.description.abstract | Missing values are very prevalent in real world; they are caused by various reasons such as user mistakes or device failures. They often cause critical problems especially in medical and healthcare application since they can lead to incorrect diagnosis or even cause system failure. Many of recent imputation techniques have adopted machine learning-based generative methods such as generative adversarial networks (GANs) to deal with missing values in medical data. They are, however, incapable of reproducing realistic time-series signals preserving important latent features such as sleep stages that are important context in many medical applications using electroencephalogram (EEG). In this study, we propose a novel GAN-based technique generating realistic EEG signal sequences which are not only shown natural but also correctly classified with sleep stages by implanting the latent features in the synthetic sequence. By experiments, we confirm that our model generates not only more realistic EEG signals than a recent GAN-based model but also preserve auxiliary information such as sleep stages. Furthermore, we demonstrate that existing machine learning methods based on EEG data still work well without sacrificing performance using the imputed data by using our method. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Contextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3126345 | - |
dc.identifier.scopusid | 2-s2.0-85119717973 | - |
dc.identifier.wosid | 000719551600001 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.9, pp 151753 - 151765 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 9 | - |
dc.citation.startPage | 151753 | - |
dc.citation.endPage | 151765 | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | WASSERSTEIN DISTANCE | - |
dc.subject.keywordPlus | SLEEP STAGES | - |
dc.subject.keywordAuthor | electroencephalogram (EEG) | - |
dc.subject.keywordAuthor | generated adversarial network (GAN) | - |
dc.subject.keywordAuthor | Missing data imputation | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9606711 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
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.