Cited 3 times in
Non-linear Analysis of Single Electroencephalography (EEG) for Sleep-Related Healthcare Applications
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 유선국 | - |
dc.contributor.author | 이충기 | - |
dc.contributor.author | 조한규 | - |
dc.date.accessioned | 2015-04-23T17:18:52Z | - |
dc.date.available | 2015-04-23T17:18:52Z | - |
dc.date.issued | 2010 | - |
dc.identifier.issn | 2093-3681 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/102226 | - |
dc.description.abstract | OBJECTIVES: Soft-computing techniques are commonly used to detect medical phenomena and to help with clinical diagnoses and treatment. The purpose of this paper is to analyze the single electroencephalography (EEG) signal with the chaotic methods in order to identify the sleep stages. METHODS: Data acquisition (polysomnography) was performed on four healthy young adults (all males with a mean age of 27.5 years). The evaluated algorithm was designed with a correlation dimension and Lyapunov's exponent using a single EEG signal that detects differences in chaotic characteristics. RESULTS: The change of the correlation dimension and the largest Lyapunov exponent over the whole night sleep EEG was performed. The results show that the correlation dimension and largest Lyapunov exponent decreased from light sleep to deep sleep and they increased during the rapid eye movement stage. CONCLUSIONS: These results suggest that chaotic analysis may be a useful adjunct to linear (spectral) analysis for identifying sleep stages. The single EEG based nonlinear analysis is suitable for u-healthcare applications for monitoring sleep | - |
dc.description.statementOfResponsibility | open | - |
dc.format.extent | 46~51 | - |
dc.relation.isPartOf | HEALTHCARE INFORMATICS RESEARCH | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.title | Non-linear Analysis of Single Electroencephalography (EEG) for Sleep-Related Healthcare Applications | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Medical Engineering (의학공학) | - |
dc.contributor.googleauthor | Chung Ki Lee | - |
dc.contributor.googleauthor | Han Gue Jo | - |
dc.contributor.googleauthor | Sun Kook Yoo | - |
dc.identifier.doi | 10.4258/hir.2010.16.1.46 | - |
dc.admin.author | false | - |
dc.admin.mapping | false | - |
dc.contributor.localId | A02471 | - |
dc.contributor.localId | A03261 | - |
dc.contributor.localId | A03919 | - |
dc.relation.journalcode | J00974 | - |
dc.identifier.eissn | 2093-369X | - |
dc.identifier.pmid | 21818423 | - |
dc.subject.keyword | Electroencephalography | - |
dc.subject.keyword | Lyapunov Exponent | - |
dc.subject.keyword | Regression Analysis | - |
dc.subject.keyword | Sleep Stage | - |
dc.subject.keyword | u-Healthcare | - |
dc.contributor.alternativeName | Yoo, Sun Kook | - |
dc.contributor.alternativeName | Lee, Chung Ki | - |
dc.contributor.alternativeName | Cho, Han Gyu | - |
dc.contributor.affiliatedAuthor | Yoo, Sun Kook | - |
dc.contributor.affiliatedAuthor | Lee, Chung Ki | - |
dc.contributor.affiliatedAuthor | Cho, Han Gyu | - |
dc.citation.volume | 16 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 46 | - |
dc.citation.endPage | 51 | - |
dc.identifier.bibliographicCitation | HEALTHCARE INFORMATICS RESEARCH, Vol.16(1) : 46-51, 2010 | - |
dc.identifier.rimsid | 51151 | - |
dc.type.rims | ART | - |
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