Detection of epileptic seizure using wavelet transformation and spike based features

Electroencephalogram (EEG) covers the detailed information regarding the neurological activity of human brain which is further used to analyze abnormal activities of which one of the abnormal activity is epileptic seizure which occurs due to sudden excitement of large number of neuron cells simultaneously. In this paper, spikes based parameters are used for epilepsy detection, as spikes are the main characteristics of seizure prone EEG signal. The signal is preprocessed by wavelet transformation and after that parameters are extracted from both normal and ictal (seizure activity) signal. Artificial Neural Network (ANN) is considered for classification and performance is measured on the basis of accuracy, sensitivity and specificity. A comparison of the proposed method with the other techniques shows the acceptable nature of this proposed method for seizure detection.