NDLI: Multifractal Analysis of Epilepsy in Electroencephalogram

Content Provider	IEEE Xplore Digital Library
Author	Aijun He Xiaodong Yang Xi Yang Xinbao Ning
Copyright Year	2007
Description	Author affiliation: Nanjing Univ., Nanjing (Aijun He; Xiaodong Yang; Xi Yang; Xinbao Ning)
Abstract	The Electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. It is highly subjective, and the symptoms may appear at random in the time scale. Traditional methods for nonlinear dynamic analysis, such as correlation dimension, Lyapunov exponent, approximate entropy, detrended fluctuation analysis, using a single parameter, cannot fully describe the extremely sophisticated behavior of EEG. The multifractal formulism reveals more "hidden" information of EEG by using singularity spectrum to characterize its nonlinear dynamics. In this paper, we explored the ability of multifractal to discriminate the EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures. The multifractal singularity spectrum of EEG signals from every group is calculated and the mean and standard variation of the range of the singularity strength, Deltaalpha , are compared. The obtained results demonstrated that the proposed method can be useful in analyzing long-term EEG signals for early detection of the electroencephalographic changes.
Starting Page	1417
Ending Page	1420
File Size	1949748
Page Count	4
File Format	PDF
ISBN	9781424410774
DOI	10.1109/ICCME.2007.4381978
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos Recurrent neural networks Fluctuations Neurons Epilepsy Fractals Electroencephalography Entropy Biomedical measurements Signal analysis
Content Type	Text
Resource Type	Article

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