文章基本信息

标题：Feature Extraction and Selection of a Combination of Entropy Features for Real-time Epilepsy Detection
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作者：B. Abhinaya ; D. Charanya ; K. Palani Thanaraj 等
期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2016
卷号：5
期号：4
页码：16073-16078
DOI：10.18535/ijecs/v5i4.03
出版社：IJECS
摘要：Epilepsy is associated with the abnormal electrical activity in the brain which is detected by recording EEG(Electroencephalogram) signals. This signal is non-linear and chaotic and hence, it is very time-consuming and tedious to analyse themvisually. In this work, we have extracted five entropy features such as Approximate Entropy, Sample Entropy, Fuzzy Entropy, PermutationEntropy and Multi-scale Entropy for characterizing the focal signals. We have used Sequential Forward Feature Selection (SFFS)algorithm to select two significant features for epilepsy classification. These two features are given as input to the Least Square SupportVector Machine (LS-SVM) classifier to differentiate normal and focal signal. The classification accuracy of our method is 82%. Moreover,the average computational time for the selected feature set is 47.94 seconds.
关键词：Epilepsy; EEG signal; Entropy; LS-SVM classifier