A novel seizure diagnostic model based on kernel density estimation and least squares support vector machine

The automated system can be an effective tool for assisting neurologists in seizure detection. However, most of the existing methods are failed to trade off the effectivity and computation cost, which is not appropriate for on-line application. In this research, we propose a novel method for dealing with 3-class electroencephalogram (EEG) problem, based upon kernel density estimation (KDE) and least squares support vector machine (LS-SVM). The filtered EEG is decomposed into several sub-bands by wavelet packet transform (WPT), then KDE is explored to calculate the corresponding probability density. Five parameters are employed for EEG representation: the maximum (Max), the skewness (Sloe), the kurtosis (Kur), the energy (En), and the central moment (CM). And significant features selected by Analysis of Variance (ANOVA) are fed to LS-SVM for pattern recognition. Furthermore, eight types of wavelet bases and four well-known functions are considered for feature extraction. Experimental results show that our approach has achieved satisfactory and comparable results for all validation methods when configured with coiflet of order 1 and uniform kernel. The highest accuracy of 10-fold cross-validation and standard 50-50 methodology is 99.40% and 99.60% with 27 and 26 features, respectively. As compared to previous literature, our proposed scheme is more suitable for diagnosis of epilepSy with higher accuracy and less number of feature that can be extracted with less computational cost. Overall, the advantages of high accuracy, easy implementation and low computational consumption have made this technique a suitable candidate for extensive clinical deployment. (C) 2017 Elsevier Ltd. All rights reserved.