title: Sleep-stage Characterization by Nonlinear EEG Analysis using Wavelet-based Multifractal Formalism.
abstract: The wavelet-based multifractal formalism was applied on sleep EEG analysis and sleep-stage characterization. The subjects used in this study were randomly selected from the MIT-BIH Polysomnographic Database. The multifractal singularity spectra of sleep EEG signals were estimated, and h<inf>0</inf>, the Hölder exponent that denotes the main singular property of the signal, was extracted from the multifractal singularity spectrum and used as sleep-stage characteristic parameter. The shift of multifractal singularity spectra of different sleep stage was observed. The mean h<inf>0</inf>exponents increased from awake to sleep stage 1, 2, 3 and 4, but decreased during rapid eye movement (REM) sleep. Our study suggests that the h<inf>0</inf>exponent could be used as an important sleep stage characteristic parameter.
authors.affiliation: Institute for Biomedical Electronic Engineering, State Key Laboratory of Modern Acoustics, Nanjing University, Nanjing 210093 China.
authors.forename: Qianli
authors.initials: Q
authors.lastname: Ma
authors.forename: Xinbao
authors.initials: X
authors.lastname: Ning
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
authors.forename: Jing
authors.initials: J
authors.lastname: Li