Multifractal Analysis of Sleep EEG Dynamics in Humans

Author

Song, I.H. ; Ji, Y.S. ; Cho, B.K. ; Ku, J.H. ; Chee, Y.J. ; Lee, J.S. ; Lee, S.M. ; Kim, I.Y. ; Kim, Sun I.

Author_Institution

Departments of Biomed. Eng., Hanyang Univ., Seoul

fYear

2007

fDate

2-5 May 2007

Firstpage

546

Lastpage

549

Abstract

The aim of this study is to investigate the possibility that human sleep EEGs can be characterized by a multifractal spectrum using wavelet transform modulus maxima (WTMM). We used sleep EEGs taken from healthy subjects during the four stages of sleep and REM sleep. Our findings showed that the dynamics in human sleep EEGs could be adequately described by a set of scales and characterized by multifractals. We performed multivariate discriminate analysis to evaluate the use of multifractal features for classification. The multivariate discriminate analysis using within-groups covariance matrices for all sleep stages yielded a total error rate of 41.8%. In conclusion, multifractal formalism, based on the WTMM, appears to be a good tool for characterizing dynamics in sleep EEGs

Keywords

covariance matrices; electroencephalography; medical signal processing; wavelet transforms; covariance matrices; multifractal analysis; multivariate discriminate analysis; sleep EEG dynamics; wavelet transform modulus maxima; Band pass filters; Electroencephalography; Fractals; Humans; Nonlinear dynamical systems; Performance analysis; Signal analysis; Sleep; Wavelet analysis; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Engineering, 2007. CNE '07. 3rd International IEEE/EMBS Conference on

Conference_Location

Kohala Coast, HI

Print_ISBN

1-4244-0792-3

Electronic_ISBN

1-4244-0792-3

Type

conf

DOI

10.1109/CNE.2007.369730

Filename

4227335