基于动态模糊决策树的心电信号分类方法  

Classification Method of Electrocardiogram Signals Based on Dynamic Fuzzy Decision Tree

作　　者：高宁化 王姮[1] 冯兴华 GAO Ninghua;WANG Heng;FENG Xinghua(School of Information Engineering,Southwest University of Science and Technology,Mianyang,Sichuan 621000,China)

机构地区：[1]西南科技大学信息工程学院

出　　处：《计算机工程》2020年第1期80-86,共7页Computer Engineering

基　　金：国家“十三五”核能开发科研项目(20161295)

摘　　要：为提高心电信号分类识别的准确率,提出一种基于时频特征融合与动态模糊决策树的心电信号分类识别方法。对心电信号依次进行周期分割、小波包分解与重构和形态识别处理,将小波包变换系数矩阵的二范数作为频域特征,并与时域特征进行融合以表征心电信号,同时将模糊C均值聚类引入模糊决策树的建树过程中,实现特征空间的动态划分。在MIT-BIH标准心电数据库上的实验结果表明,该方法的分类识别准确率较高,心电信号正异常分类的准确率达99.14%。To improve the classification and recognition accuracy of Electrocardiogram(ECG)signals,this paper proposes a classification and recognition method of ECG signals based on time-frequency feature fusion and Dynamic Fuzzy Decision Tree(DFDT).ECG signals are processed successively by periodic segmentation,wavelet packet decomposition and reconstruction,and pattern recognition.The 2-norm of the coefficient matrix of wavelet packet transform is taken as frequency domain features,and is fused with time domain features to represent ECG signals.Fuzzy C-Means(FCM)clustering is introduced into the building of fuzzy decision trees to achieve dynamic partition of feature space.Experimental results on MIT-BIH standard ECG database show that the proposed method has a high classification and recognition accuracy rate,reaching 99.14%for normal and abnormal ECG signals.

关 键 词：心电信号 小波包 特征融合 动态模糊决策树 模糊C均值聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]