title: Non-linear Analysis of Single Electroencephalography (EEG) for Sleep-Related Healthcare Applications.
abstract: OBJECTIVE
  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.
authors.affiliation: Deptartment of Medical Engineering, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Chung Ki
authors.initials: CK
authors.lastname: Lee
authors.forename: Han Gue
authors.initials: HG
authors.lastname: Jo
authors.forename: Sun Kook
authors.initials: SK
authors.lastname: Yoo