Identification of Suitable Basis Wavelet Function for Epileptic Seizure Detection Using EEG Signals

Selection of suitable order of Daubechies (DB) wavelet for the decomposition of Electroencephalogram (EEG) signals to detect epileptic seizures is quite challenging, as experimentation is time-consuming. In existing methods, the selection of basis wavelet function for decomposition of EEG signals is carried out by considering the literature or by trial and error method. There is a very little significant literature which discusses the comparative analysis for the identification of suitable basis wavelet function (mother wavelet). However, the existing methods often fail to provide proper justification for selecting the mother wavelets. Hence, this research work addresses the fore-mentioned setback by identifying the suitable basis wavelet function based on wavelet selection methods for epileptic seizure detection. Further, the entropy-based features are extracted and classified using SVM, DT, ANN, and KNN with five complex cases (University of Bonn, Germany EEG dataset): A-B-C-D-E, AB-CD-E, C-D-E, AB-C-D, and ABCD-E. From the entropy analysis, it is evident that while extracting entropy-based features, tenth order of Daubechies wavelet (DB10) is found to be the most suitable basis wavelet function for the accurate detection of Epileptic Seizures. The performance metrics confirm the suitability of the identified basis wavelet function in terms of sensitivity, specificity and classification accuracy.