Analysis of biomedical EEG signals using Wavelet Transforms and Multifractal Analysis

Fractal Analysis is the well developed theory in the Non-linear Analysis of Biomedical Signals such as Electroencephalogram (EEG). EEG signal is essentially multi scale fractal, i.e. Multifractal. Therefore Multifractal measures such as Generalized Fractal Dimensions (GFD), could be a useful tool to compute the degree of disorders, complexity, irregularity and chaotic nature of the Biomedical Signals of the Epileptic patients. We organized a novel scheme for detecting epileptic seizures from EEG data recorded from Healthy subjects and Epileptic patients. The scheme was based on GFD and the Discrete Wavelet Transform (DWT) analysis of EEG signals. First EEG signals were decomposed into approximation and detail coefficients using DWT and then GFD values of the original EEGs, approximation and detail coefficients were computed. Significant differences were found between the GFD values of the Healthy and Epileptic EEGs showing us to detect seizures with high accuracy. Without DWT as preprocessing step, it was shown that the detection rate is very less. The proposed idea was demonstrated through the graphical and statistical tools. Hence we conclude that the Multifractal Analysis based on GFD and the Wavelet Decomposition through DWT are the strong detectors and indicators of the state of illness of the Epileptic Patients.