Epilepsy detection from electroencephalogram signal using singular value decomposition and extreme learning machine classifier

Automatic detection of seizure plays an important role for both long-term monitoring and diagnosis of epilepsy. In this work, the proposed singular value decomposition-extreme learning machine (SVD-ELM) classifier technique provide good generalised performance with a remarkable fast learning speed in comparison to existing conventional techniques. Here, both feature extraction and classification of EEG signal has been done for detection of epileptic seizure of human brain, taking Bonn University dataset. Proposed method is based upon the multi-scale eigenspace analysis of the matrices generated using discrete wavelet transform (DWT) of EEG signal by SVD at substantial scale and are classified using extracted singular value features and extreme learning machine (ELM) with dissimilar activation functions. The proposed SVD-ELM technique has been applied for the first time on EEG signal for epilepsy detection using five class classification which produces overall accuracy of 95% (p < 0.001) with sine and radbas activation function.