Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data

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.

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