Non-linear Analysis of Single Electroencephalography (EEG) for Sleep-Related Healthcare Applications.
- Author:
Chung Ki LEE
1
;
Han Gue JO
;
Sun Kook YOO
Author Information
- Publication Type:Original Article
- Keywords: Regression Analysis; Electroencephalography; Sleep Stage; Lyapunov Exponent; u-Healthcare
- MeSH: Delivery of Health Care; Electroencephalography; Humans; Light; Male; Regression Analysis; Sleep Stages; Sleep, REM; Young Adult
- From:Healthcare Informatics Research 2010;16(1):46-51
- CountryRepublic of Korea
- Language:English
- 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.
