Epileptic seizure detection using singular values and classical features of EEG signals

In this paper, an epileptic seizure event detection algorithm utilizing five features namely singular values, total average power, delta band average power, variance and mean, is proposed. Using CHB-MIT Scalp EEG Database, the calculations of the features are performed over a sliding window of one second. The algorithm was evaluated in terms of accuracy, sensitivity, specificity and failure rate. This investigation used SVM as the classification technique. The performance comparisons are made with techniques based on classical features alone, singular value alone and combination of classical features and singular values. The results show that the proposed algorithm achieves better results than using singular values alone or using classical features alone with an average accuracy of 94.82%.