Non-linear Analysis of Single Electroencephalography (EEG) for Sleep-Related Healthcare Applications / 대한의료정보학회지
Healthcare Informatics Research
; : 46-51, 2010.
Article
em En
| WPRIM
| ID: wpr-152070
Biblioteca responsável:
WPRO
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.
Palavras-chave
Texto completo:
1
Índice:
WPRIM
Assunto principal:
Sono REM
/
Fases do Sono
/
Análise de Regressão
/
Atenção à Saúde
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Eletroencefalografia
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Luz
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
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Male
Idioma:
En
Revista:
Healthcare Informatics Research
Ano de publicação:
2010
Tipo de documento:
Article