基于TQWT的癫痫脑电信号的识别.doc

基于TQWT的癫痫脑电信号的识别.doc基于TQWT的癫痫脑电信号的识别
贺王鹏杨琳王芳黄绍平
西安电子科技大学空间科学与技术学院西安交通大学第二附属医院
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摘    要:
针对癫痫脑电(EEG) 信号的识别问题, 提出了一种基于可调品质因子小波变换(TQWT) 的脑电特征提取方法。首先, 利用TQWT将EEG信号进行分解, 得到各个小波子波带;然后, 根据癫痫异常波对应的频率范围, 合理的选择小波子波带进行重构, 提取有效值和峰峰值构成特征分量;最后, 采用支持向量机进行分类。将所提出方法应用于癫痫脑电信号的识别中, 以德国伯恩大学癫痫研究中心采集的典型脑电数据进行验证。实验分析结果表明, 所提出的特征提取方法对正常和癫痫发作期EEG信号的分类准确率可达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;
作者简介:杨琳, Email:yr_1217@
收稿日期:2016-11-13
基金:西安交通大学临床新技术项目(XJLS-2015-179)
Identification of Epileptic EEG Signals based on the Tunable Q-factor Wavelet Transform
HE Wangpeng YANG Lin WANG Fang HUANG Shaoping
School of Aerospace Science and Technology, Xidian University; Second Affiliated Hospital of Xi'an Jiaotong University;
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 pose EEG signal into several wavelet subbands. Then, according to the frequency band of epileptic abnormal waves, the EEG signal was reconstructed adaptively via corresponding TQWT wavelet subbands. The root mean square value and peak-to-peak value indicators were calculated as feature vector. Finally, the support vector machine ( SVM) was introduced for classification. Moreover, the proposed method was applie