Application of wavelet fractal features for the automated detection of epileptic seizure using electroencephalogram signals
by Rahul Upadhyay; Swati Jharia; Prabin Kumar Padhy; Pavan Kumar Kankar
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 19, No. 4, 2015

Abstract: 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 (bior1.1 and bior2.2), Coiflets (coif1-coif3) and Daubechies (Db1-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.

Online publication date: Wed, 02-Dec-2015

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