Classifying the epilepsy EEG signal by hybrid model of CSHMM on the basis of clinical features of interictal epileptiform discharges

Abstract

Many methods of processing epileptic EEG signals are concentrated in the classification, and most of them use the wavelet transform and SVM classification algorithm. Although these algorithms acquire the high accuracy, it is still unable to provide a good explanation of quantitative difference and physical meaning between epileptic EEG and normal EEG. This paper presents a new hybrid algorithm (CWT-SVM-HMM) to classify epileptic EEG signal. By the results of classification of HMM, we can track back abnormal signal frequency sources, through the analysis of the sources of seizures during different frequency band, we can get a seizure of accurate quantitative analysis according to clinical feature of interictal epileptiform discharges.