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수면단계 분석을 위한 특징 선택 알고리즘 설계

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dc.contributor.author유선국-
dc.date.accessioned2014-12-18T09:39:35Z-
dc.date.available2014-12-18T09:39:35Z-
dc.date.issued2013-
dc.identifier.issn1016-135X-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/88586-
dc.description.abstractThe aim of this study is to design a classifier for sleep stage analysis and select important feature set which shows sleep stage well based on physiological signals during sleep. Sleep has a significant effect on the quality of human life. When people undergo lack of sleep or sleep-related disease, they are likely to reduced concentration and cognitive impairment affects, etc. Therefore, there are a lot of research to analyze sleep stage. In this study, after acquisition physiological signals during sleep, we do pre-processing such as filtering for extracting features. The features are used input for the new combination algorithm using genetic algorithm(GA) and neural networks(NN). The algorithm selects features which have high weights to classify sleep stage. As the result of this study, accuracy of the algorithm is up to 90.26% with electroencephalography(EEG) signal and electrocardiography(ECG) signal, and selecting features are alpha and delta frequency band power of EEG signal and standard deviation of all normal RR intervals(SDNN) of ECG signal. We checked the selected features are well shown that they have important information to classify sleep stage as doing repeating the algorithm. This research could use for not only diagnose disease related to sleep but also make a guideline of sleep stage analysis.-
dc.description.statementOfResponsibilityopen-
dc.relation.isPartOfJournal of the Institute of Electronics and Information Engineers of Korea (전자공학회논문지)-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.title수면단계 분석을 위한 특징 선택 알고리즘 설계-
dc.title.alternativeThe Design of Feature Selecting Algorithm for Sleep Stage Analysis-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Medical Engineering (의학공학)-
dc.contributor.googleauthor이지은-
dc.contributor.googleauthor유선국-
dc.identifier.doi10.5573/ieek.2013.50.10.207-
dc.admin.authorfalse-
dc.admin.mappingfalse-
dc.contributor.localIdA02471-
dc.relation.journalcodeJ01784-
dc.identifier.pmid수면 ; 뇌파 ; 심전도 ; 유전알고리즘 ; 신경망 ; Sleep ; EEG ; ECG ; GA ; NN-
dc.identifier.urlhttp://dx.doi.org/10.5573/ieek.2013.50.10.207-
dc.subject.keyword수면-
dc.subject.keyword뇌파-
dc.subject.keyword심전도-
dc.subject.keyword유전알고리즘-
dc.subject.keyword신경망-
dc.subject.keywordSleep-
dc.subject.keywordEEG-
dc.subject.keywordECG-
dc.subject.keywordGA-
dc.subject.keywordNN-
dc.contributor.alternativeNameYoo, Sun Kook-
dc.contributor.affiliatedAuthorYoo, Sun Kook-
dc.rights.accessRightsfree-
dc.citation.volume50-
dc.citation.number10-
dc.citation.startPage207-
dc.citation.endPage216-
dc.identifier.bibliographicCitationJournal of the Institute of Electronics and Information Engineers of Korea (전자공학회논문지), Vol.50(10) : 207-216, 2013-
dc.identifier.rimsid34403-
dc.type.rimsART-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers

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