Multiclass classification of EEG signal for epilepsy detection using DWT based SVD and fuzzy k NN classifier

Abstract

Epileptic seizures happen because of neuronal disorder that produces an unusual pattern of brain signals. Automatic seizure detection has proved to be a challenging task, for both long terms monitoring as well as epilepsy diagnosis. In this work, the proposed discrete wavelet transform (DWT) based singular value decomposition fuzzy k-nearest neighbor classifier (SVD-FkNN) technique, is one of the most effective methods in supervised learning, which provides good accuracy with fast learning speed in comparison to several other conventional techniques. In this work, both feature extraction and classification of EEG signals have been done for epilepsy detection of the human brain using the Bonn University dataset. The proposed method is based on the multi-scale eigenspace analysis of the matrices, generated from the sub-band signals of all EEG channels using DWT by SVD at a substantial scale and are classified using extracted singular value features and FkNN classifier with different ‘k’ values to obtain better accuracy. The proposed DWT based SVD-FkNN technique has been applied for the first time on the EEG signal for epilepsy detection (using five-class classifications). The experimental results of the proposed method give an overall accuracy of 100% for two and three class classification and 93.33% ( $p<$ 0.001) for five class classification.