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An adaptive unsupervised classification of seizure method based on LMDMSSE with EEG signals

Hao Ren, Jianfeng Qu, Yi Chai, Qiu Tang, Yuming Zhou

On the basis of analyzing electroencephalogram (EEG) signals with nonlinear and nonstationary troubles, an adaptive unsupervised classification of seizure method, based on LMD-MSSE, is introduced into this article. The local mean decomposition (LMD), multi-scales sample entropy (MSSE) and the unsupervised classifier called K-nearest neighbors (KNN) are integrated in this method. Particularly, the LMD is utilized to obtain different component signals, adaptively, called product functions (PFs), and then using the MSSE to analyze these PFs to obtain sample entropies over different scales, which can describe the features of different status epileptics. In addition, KNN is employed to make full use of the typical morbid state features to detect the epilepsy. In order to show the superiority over this method, a series of epileptics are treated as an example and LMD-MSSE is employed to extract the features from EEG signals, and then the KNN classifier, Support Vector Machine (SVM) and Back Propagation Neural Network (BP) are all utilized to identify the different status epileptics. A comparison between the result obtained by this method and result adopted by previous studies with the same database (A-E) shows that the LMD-MSSE method has provided excellent performances in automatic seizure detection, which can be employed to realize the adaptive unsupervised epilepsy recognition.