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Non-linear Analysis of Single Electroencephalography (EEG) for Sleep-Related Healthcare Applications

Authors
 Chung Ki Lee  ;  Han Gue Jo  ;  Sun Kook Yoo 
Citation
 HEALTHCARE INFORMATICS RESEARCH, Vol.16(1) : 46-51, 2010 
Journal Title
HEALTHCARE INFORMATICS RESEARCH
ISSN
 2093-3681 
Issue Date
2010
Keywords
Electroencephalography ; Lyapunov Exponent ; Regression Analysis ; Sleep Stage ; u-Healthcare
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
Files in This Item:
T201003449.pdf Download
DOI
10.4258/hir.2010.16.1.46
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
Lee, Chung Ki(이충기)
Cho, Han Gyu(조한규)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/102226
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