Application of wavelet fractal features for the automated detection of epileptic seizure using electroencephalogram signals. (2015)

• Record Type: Journal Article
• Authors: Upadhyay, Rahul; Jharia, Swati; Padhy, Prabin Kumar; Kankar, Pavan Kumar
• Is Part Of: International journal of biomedical engineering and technology. Volume 19:Number 4(2015)
• Page Start: 355
• Page End: 372
• Publication Date: 2015
• Subjects: 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; Biomedical engineering -- Periodicals; 610.28
• Languages: English
• ISSNs: 1752-6418

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.