Application of wavelet fractal features for the automated detection of epileptic seizure using electroencephalogram signals

摘要

In this paper, an attempt is made to find the appropriate wavelet function and wavelet-based fractal features for automated detection of epileptic seizure. Electroencephalogram (EEG) signals considered in this study include seizure and non-seizure EEG signals. Proposed study is occurred in four steps. In the first step, six frequency sub-bands of EEG signals (seizure and non-seizure) are computed using wavelet functions such as Haar, Biorthogonal (biorl.l and bior2.2), Coiflets (coifl-coif3) and Daubechies (Dbl-Db3). In the second step, wavelet thresholding is performed for undesirable noise suppression. Further, fractal dimensions are calculated from thresholded wavelet coefficients of four sub-bands as features in the third step. In the fourth step, the prepared feature vectors are fed to the artificial intelligence techniques for classifying seizure and non-seizure EEG signals. For classification three artificial intelligence techniques, i.e. least square-support vector machine, artificial neural network and random forest tree classifiers, are employed. Experimental result shows the effectiveness of the proposed methodology for epileptic seizure detection.