文章基本信息

标题：ICTAL EPILEPSY AND NORMAL EEG FEATURE EXTRACTION BASED ON PCA, KNN AND SVM CLASSIFICATION
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作者：SISWANDARI NOERTJAHJANI ; ADHI SUSANTO ; RISANURI HIDAYAT 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：83
期号：1
出版社：Journal of Theoretical and Applied
摘要：Driven by a deep interest to find some spesific epilepsy EEG signal features as compared with normal ones. An array of electrodes, normaly the FP1, FP2, F7, F3, F2, F4, F8, C3, CZ, C4, T3, T4, T5, T6, P3, P4, PZ, O1, and OZ. The recorder signals were than processed and the standard sets of statistical quantities of means, variances, skewnesses, kurtosises, entropies, minima and maxima. Principal Component Analysis (PCA) were applied to these quantities to acquire two major one representing each quantity which separate best between epilepsy ictal and normal persons resorting to the SVM and KNN classification algorithms. The results show that the PCA elevates accuracy significantly and KNN achieves the mission better than SVM.
关键词：Ictal; Normal; PCA; KNN; SVM