title: Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data.
abstract: A number of natural time series including electroencephalogram (EEG) show highly non-stationary characteristics in their behavior. We analyzed the EEG in sleep apnea that typically exhibits non-stationarity and long-range correlations by calculating its scaling exponents. Scaling exponents of the EEG dynamics are obtained by analyzing its fluctuation with detrended fluctuation analysis (DFA), which is suitable for non-stationary time series. We found the mean scaling exponents of EEG is discriminated according to Non-REM, REM (Rapid Eye Movement) and waken stage, and gradually increased from stage 1 to stage 2, 3 and 4.
authors.affiliation: Department of Biomedical Engineering, College of Medicine, Hanyang University Haengdang-dong, Seongdong-ku, 133-791, Seoul, South Korea.
authors.forename: Jong Min
authors.initials: JM
authors.lastname: Lee
authors.forename: Dae Jin
authors.initials: DJ
authors.lastname: Kim
authors.forename: In Young
authors.initials: IY
authors.lastname: Kim
authors.forename: Kwang Suk
authors.initials: KS
authors.lastname: Park
authors.forename: Sun I
authors.initials: SI
authors.lastname: Kim
mesh_terms: D002540:Cerebral Cortex / Q000503:physiopathology; D006801:Humans; D008432:Mathematical Computing; D017711:Nonlinear Dynamics; D017286:Polysomnography; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology; D012894:Sleep Stages / Q000502:physiology