Multifractal Analysis of Epilepsy in Electroencephalogram

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.