关键词: 癫痫小鼠模型, 小波变换, 特征融合, 支持向量机

Abstract: The Electroencephalogram（EEG） of mouse model of epilepsy in normal and epileptic status is collected to study the automatic classification of epileptic EEG. The noise- and artifact-attenuated EEG is wavelet-transformed, and the linear feature（standard deviation） and the nonlinear feature（sample entropy） are then extracted for the EEG signals and those wavelet coefficients related to the characteristic waveforms of epileptic EEG. Classification is implemented using support vector machine with above individual features and their combinations. The classification accuracy based on the standard deviation and sample entropy of EEG signals are 59.1% and 58.00%, respectively.The accuracy increases to 86.60% or 88.60%, when the standard deviations or sample entropies of relevant wavelet coefficients are used as input features. After combining both types of features, the classification accuracy is 99.80%. These results show that wavelet coefficient features fusion significantly improves the classification accuracy, achieving effective classification of mouse epileptic EEG.

Key words: epileptic mice model, wavelet transform, feature fusion, support vector machine