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

Authors: Rahul Upadhyay; Swati Jharia; Prabin Kumar Padhy; Pavan Kumar Kankar

Addresses: PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482005, Madhya Pradesh, India ' PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482005, Madhya Pradesh, India ' PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482005, Madhya Pradesh, India ' PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482005, Madhya Pradesh, India

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.

Keywords: epilepsy; EEG signals; electroencephalograms; DWT; discrete wavelet transform; fractal dimension; artificial intelligence; wavelet fractal features; automated detection; epileptic seizures; seizure detection; least squares; support vector machines; SVM; artificial neural networks; ANNs; random forest; classifiers.

DOI: 10.1504/IJBET.2015.073426

International Journal of Biomedical Engineering and Technology, 2015 Vol.19 No.4, pp.355 - 372

Received: 26 Feb 2015
Accepted: 04 Jun 2015
Published online: 02 Dec 2015 *

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