生物医学工程研究2013，Vol.32Issue(2)：74-79,6.
近似熵与SVM在自动分类癫痫脑电信号中的应用
The Application of Approximate Entropy and Support Vector Machine in Classifying Signal of Epilepsy
摘要
Abstract
To classify the EEG signals into interictal EEGs and ictal EEGs by the method of combination with ApEn and SVM,examine whether the nonlinear dynamic index can be effectively used in automatic detection of EEG epilepsy wave and the generalize ability of classifier trained by non-linear dynamics through the classification result.We used EEG from epileptic patient to train SVM and used it to classify EEG from other epileptic patients.It show that the SVM classifier practiced by nonlinear dynamic characteristics has a good generalizing ability; the classifier achieves a good classification result to different epileptic patients.关键词
癫痫/EEG/近似熵/支持向量机/实时探测/分类Key words
Epilepsy/EEG/Approximate entropy (ApEn)/Support vector machine (SVM)/Real-time detection/Classification分类
医药卫生引用本文复制引用
张振,杜守洪,陈子怡,田翔华,周毅,张洋..近似熵与SVM在自动分类癫痫脑电信号中的应用[J].生物医学工程研究,2013,32(2):74-79,6.基金项目
国家自然科学基金项目(61263011,81000554) （61263011,81000554）
中央高校基本科研业务费中山大学培育项目(11ykpy07) （11ykpy07）
广东省自然科学基金项目(S2011010005309) （S2011010005309）
新疆医科大学创新基金(XJC201209).. （XJC201209）
