基于变分模态分解的癫痫脑电信号分类方法

癫痫是一种常见的脑部疾病，通过脑电图能非侵入地定位人脑中的致痫区域.为了辨别病灶性和非病灶性癫痫脑电信号，文章提出一种基于变分模态分解的癫痫脑电信号自动检测方法，首先将原信号分割成多个子信号，并对各子信号进行变分模态分解，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和精细复合多尺度模糊熵两个特征并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94.24%，95.58%和90.64%，ROC曲线下面积达0.978.

An Epileptic Electroencephalogram Signal Classification Method Based on Variational Mode Decomposition

Epilepsy is a recurrent cerebral disease,and electroencephalogram (EEG) provides a non-invasive way to identify epileptogenic sites in the brain.In order to distinguish focal and non-focal epilepsy EEG signals,this paper proposes an automated epileptic EEG detection method based on variational mode decomposition.Firstly,the original signals are divided into several sub-signals,which are decomposed into intrinsic mode functions by using the variational mode decomposition (VMD).Furthermore,refined composite multiscale dispersion entropy (RCMDE) and refined composite multiscale fuzzy entropy (RCMFE) are extracted from each intrinsic mode function.Finally,the support vector machine (SVM) is used to classify characteristics.For an epilepsy EEG signals' public data set,the final experimental performance measures of accuracy,sensitivity,and specificity reach 94.24%,95.58% and 90.64% respectively,and the area under the ROC curve is 0.978.