Mutifractal analysis of electroencephalogram time series in humans

Authors:
In-Ho Song;Sang-Min Lee;In-Young Kim;Doo-Soo Lee;Sun I. Kim
Affiliations:
Department of Electrical and Computer Engineering, Hanyang University, Korea;Division of Bionics & Bioinformatics, College of Engineering, Chonbuk National University, Deokjin-dong, Jenju, Korea;Department of Biomedical Engineering, College of Medicine, Hanyang University, Seoul, Korea;Department of Electrical and Computer Engineering, Hanyang University, Korea;Department of Biomedical Engineering, College of Medicine, Hanyang University, Seoul, Korea
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

By analyzing electroencephalograms taken from healthy subjects and epilepsy patients, we investigated whether the complexity of the electroencephalogram (EEG) could be characterized by a multifractal. Our results showed that the EEGs from the two sets exhibit higher complexity than monofractal 1/f scaling. A significant finding was the observation that the dynamics of the epileptic EEGs exhibited anticorrelated, correlated, and uncorrelated behaviors. In conclusion, multifractal formalism based on the wavelet transform modulus maxima (WTMM) may be a good tool to characterize the various dynamics of the two sets.