生物医学工程研究2017，Vol.36Issue(4)：346-350,5.DOI:10.19529/j.cnki.1672-6278.2017.04.14
基于TQWT的癫痫脑电信号的识别
Identification of Epileptic EEG Signals based on the Tunable Q-factor Wavelet Transform
摘要
Abstract
For the problem of identifying and classifying epileptic EEG signals , we proposed an effective technique based on the tunable Q-factor wavelet transform ( TQWT) .Firstly, the TQWT was employed to decompose EEG signal into several wavelet subba-nds.Then, according to the frequency band of epileptic abnormal waves , the EEG signal was reconstructed adaptively via correspond-ing TQWT wavelet subbands .The root mean square value and peak -to-peak value indicators were calculated as feature vector .Fi-nally, the support vector machine (SVM) was introduced for classification.Moreover, the proposed method was applied to analyze real EEG data collected from the Epilepsy Research Center , University of Bonn, Germany.The results demonstrate that the proposed tech-nique can effectively detect epilepsy disease from EEG signals with good classification accuracy of 98%.关键词
癫痫脑电/可调品质因子小波变换/支持向量机/特征提取/分类Key words
Epileptic EEG/Tunable Q-factor wavelet transform/Support vector mahine -fator waveler transform/Feature ex-traction/Classification分类
医药卫生引用本文复制引用
贺王鹏,杨琳,王芳,黄绍平..基于TQWT的癫痫脑电信号的识别[J].生物医学工程研究,2017,36(4):346-350,5.基金项目
西安交通大学临床新技术项目(XJLS-2015-179). （XJLS-2015-179）
