基于弹性变分模态分解的癫痫脑电信号分类方法

eEpileptic electroencephalogram signal classification method based on elastic variational mode decomposition

癫痫脑电信号分类对于癫痫诊治具有重要意义.为了实现病灶性与非病灶性癫痫脑电信号的分类,本文利用弹性网回归重构变分模态分解算法,提出弹性变分模态分解算法并将其应用到所提癫痫脑电信号分类方法中.该方法先将原信号分割成多个子信号,并对各子信号进行弹性变分模态分解,然后从分解后的不同变分模态函数中提取精细复合多尺度散布熵作为特征,最后利用支持向量机进行分类.针对癫痫脑电的公共数据集,最终的实验结果表明,准确率、灵敏度和特异度三个性能指标分别达到92.54%,93.22%和91.86%.

Epilepsy is an extensive nervous system disease nowadays.Electroencephalogram(EEG)can capture the abnormal discharge of nerves in the brain duration of seizure and provide a non-invasive way to identify epileptogenic sites in the brain.In order to distinguish between focal epilepsy EEG signal and non-focal epilepsy EEG signal,in this paper we propose an automated epileptic EEG detection method based on the elastic variational mode decomposition(EVMD).The proposed EVMD algorithm is a method of analyzing the signals and also a processing method in time-frequency domain,in which the elastic net regression is used to reconstruct a constrained variational model in variational mode decomposition(VMD).Used in the VMD algorithm is the Tikhonov regularization that is also statistically called ridge regression as a solution of recovering the unknown signal and assessing the bandwidth of a mode,namely the variational equation constructed by VMD only has L2 norm.However,the ridge regression cannot select variables when the equation has multiple variables.Another regression method,called lasso regression,only has L1 norm and can select a more accurate model from multiple variables,but it has worse performance when variables have group effect or co-linearity.The elastic net regression has advantages of ridge regression and lasso regression,in other word,the variational equation constructed by EVMD has both L1 regularization item and L2 regularization item,so in this paper we propose the EVMD by elastic net regression.In addition,in this paper the EVMD is used to distinguish between focal epilepsy EEG signal and non-focal epilepsy EEG signal.Firstly,the original EEG signals are divided into several sub-signals where the test signals are divided into sub-signals with shorter durations by time series and a reasonable time overlap is kept between successive sub-signals.After that each sub-signal is decomposed into intrinsic mode functions by using the EVMD.Furthermore,the refined composite multiscale dispersion entropy(RCMDE)as feature is extracted from each intrinsic mode function where a Student’s t-test is used to assess the statistical differences between RCMDEs extracted from focal and non-focal EEG signals respectively.Finally,the support vector machine(SVM)is used to classify their features.For an epilepsy EEG signalspublic data set,the final experimental results show that the performance indices of accuracy,sensitivity,and specificity can reach 92.54%,93.22%and 91.86%respectively.