A comparative study of feature ranking techniques for epileptic seizure detection using wavelet transform

In this paper, an attempt is made to obtain optimal wavelet function and wavelet based Electroencephalograph (EEG) features for detection of epilepsy using appropriate feature ranking techniques. The EEG data includes normal, pre-ictal and ictal EEG signals. Initially, signals are decomposed using 16 discrete wavelets and the best basis wavelet is selected using Maximum Energy to Permutation Entropy ratio criterion. A range of statistical, fractal and entropy based features are calculated from selected wavelet coefficients. The performance of three different feature ranking techniques i.e. Fisher Score, ReliefF and Information Gain is investigated on computed features. Classification of the ranked features is performed by machine learning technique Least Square-Support Vector Machine. Features ranked through Fisher Score ranking technique show high discrimination ability and classified with high classification accuracy. Classification results ensure the suitability of proposed best basis wavelet based feature extraction methodology and Fisher Score ranking technique in epilepsy detection.

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