MULTIFRACTAL-WAVELET BASED DENOISING IN THE CLASSIFICATION OF HEALTHY AND EPILEPTIC EEG SIGNALS

Identification of abnormality in Electroencephalogram (EEG) signals is the vast area of research in the neuroscience. Especially, the classification of healthy and epileptic subjects through EEG signals is the crucial problem in the biomedical sciences. Denoising of EEG signals is another important task in signal processing. The noises must be corrected or reduced before the subsequent decision analysis. This paper presents a wavelet-based denoising method for the recovery of EEG signal contaminated by nonstationary noises and investigates the recognition of healthy and epileptic EEG signals by using multifractal measures such as Generalized Fractal Dimensions. The multifractal measures show the significant differences among normal, interictal and epileptic ictal EEGs with denoising by wavelet transform as the pre-processing step. The denoised artifact-free EEG presents a very good improvement in the identification rate of epileptic seizure. The proposed scheme illustrates with high accuracy through the suitable graphical and statistical tools and performs an important role in the epileptic seizure detection.