A statistical method for automatic detection of seizure and epilepsy in the dual tree complex wavelet transform domain

In this paper, a statistical method for automatic detection of seizure and epilepsy in the dual-tree complex wavelet transform(DT-CWT) domain is proposed. Variances calculated from the EEG signals and their DT-CWT sub-bands are utilized as features in the classifiers such as artificial neural network(ANN) and support vector machine(SVM). Studies are conducted using EEG signals from a publicly available benchmark EEG database to assess the ability of the proposed method for a number of clinically relevant classification scenario which include healthy vs seizure, healthy and non-seizure(inter-ictal) vs seizure(ictal), and finally, ictal vs inter-ictal records. It is shown that the proposed method using SVM performs better than employing ANN. It gives 100% accuracy, sensitivity and specificity; at least the same or better than those corresponding to several existing techniques. In addition, the proposed method is computationally faster than the time-frequency and EMD-based techniques.