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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This paper demonstrates the superiority of energy-based features derived from the knowledge of predominant-pitch, for singing voice detection in polyphonic music over commonly used spectral features. However, such energy-based features tend to misclassify loud, pitched instruments. To provide robustness to such accompaniment we exploit the relative instability of the pitch contour of the singing voice by attenuating harmonic spectral content belonging to stable-pitch instruments, using sinusoidal modeling. The obtained feature shows high classification accuracy when applied to north Indian classical music data and is also found suitable for automatic detection of vocal-instrumental boundaries required for smoothing the frame-level classifier decisions.