生物医学工程研究2017，Vol.36Issue(1)：33-37,5.DOI:10.19529/j.cnki.1672-6278.2017.01.07
经验模式分解与代价敏感支持向量机在癫痫脑电信号分类中的应用
Classification and Prediction of EEG based on Empirical Mode Decomposition
摘要
Abstract
EEG signals can be extracted from EEG signals, which can better understand the characteristics of EEG signals.However, due to the aliasing of various types of external signals, the signal exhibits nonlinear and nonstationarity.Therefore, for EEG signals, extraction is a problem.In this paper, an empirical mode decomposition (EMD) algorithm, which is superior to wavelet decomposition, was proposed to decompose the EEG signal and extract the eigenvalues of the main IMF components.Then, the cost-sensitive support vector machine (CSVM) was used to classify the parameters excellent.In the study of EEG signals of epilepsy patients, the accuracy of classification is more than 90%, which verifies the feasibility of this method.关键词
脑电信号/癫痫/经验模式分解/代价敏感支持向量机/参数寻优Key words
Electroencephalogram/Epilepsy/Empirical mode decomposition/Cost-sensitive SVM/Parameter optimization分类
医药卫生引用本文复制引用
李冬梅,张洋,杨日东,陈子怡,田翔华,董楠,尔西丁·买买提,周毅..经验模式分解与代价敏感支持向量机在癫痫脑电信号分类中的应用[J].生物医学工程研究,2017,36(1):33-37,5.基金项目
国家自然科学基金资助项目(61263011) （61263011）
中央高校基本业务费项目(15ykcj03d) （15ykcj03d）
广东省前沿与关键技术创新专项(2014B010118003,2015B010106008). （2014B010118003,2015B010106008）
