In this paper, a novel hierarchical multi-class SVM (H-MSVM) with extreme learning machine (ELM) as kernel is proposed to classify electroencephalogram (EEG) signals for epileptic seizure detection. A clinical EEG benchmark dataset having five classes, obtained from Department of Epileptology, Medical Center, University of Bonn, Germany, is considered in this work for validating the clinical utilities. Wavelet transform-based features such as statistical values, largest Lyapunov exponent, and approximate entropy are extracted and considered as input to the classifier. In general, SVM provides better classification accuracy, but takes more time for classification and also there is scope for a new multi-classification scheme. In order to mitigate the problem of SVM, a novel multi-classification scheme based on hierarchical approach, with ELM kernel, is proposed. Experiments have been conducted using holdout and cross-validation methods on the entire dataset. Metrics namely classification accuracy, sensitivity, specificity, and execution time are computed to analyze the performance of the proposed work. The results show that the proposed H-MSVM with ELM kernel is efficient in terms of better classification accuracy at a lesser execution time when compared to ANN, various multi-class SVMs, and other research works which use the same clinical dataset.

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The present work proposes two novel approaches namely One Dimensional adaptive average Local Binary Pattern (1-D AaLBP) and One-Dimensional adaptive difference Local Binary Pattern (1-D AdLBP) for feature extraction from EEG signals and Convolutional Neural Network (CNN) for classification of EEG signals. Both the proposed feature extraction methods are computationally easy to implement. In the first step the histograms are formed from the extracted patterns, after that feature vectors of the histogram are given as input to the classifier. Two benchmark EEG datasets such as Bonn and CHB-MIT are employed for conducting experiments for comparing the performances of the proposed method with other existing research works. The performance measures such as sensitivity, specificity, classification accuracy and execution time are used for evaluating the proposed methods. It is learned from the experiments conducted that among various methods the proposed method provides improved performance in terms of sensitivity, specificity, classification accuracy and execution time.

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