基于EEMD与多重分形的心电信号特征提取与分类  

Feature Extraction and Classification of ECG Signals Based on EEMD and Multifractal

作　　者：叶莉华 李秋生[1,2] 卢清[1] YE Lihua;LI Qiusheng;LU Qing(School of Physics and Electronic Information,Gannan Normal University,Ganzhou,Jiangxi 341000,China;Research Center of Intelligent Control Engineering Technology,Gannan Normal University,Ganzhou,Jiangxi 341000,China)

机构地区：[1]赣南师范大学物理与电子信息学院,江西赣州341000 [2]赣南师范大学智能控制工程技术研究中心,江西赣州341000

出　　处：《信号处理》2023年第1期143-153,共11页Journal of Signal Processing

基　　金：国家自然科学基金(61561004);江西省研究生创新专项资金项目(YC2021-S739);江西省教育厅科技项目(GJJ190772)。

摘　　要：心电信号的快速分类在心脏病医学诊断领域具有至关重要的作用,为了降低人工识别的成本,提高心电信号分类的准确率。文章以正常搏动、房性早搏、室性早搏、左束支传导阻滞及右束支传导阻滞信号为研究对象,用集合经验模态分解分解心电信号,并结合相关系数来选取本征模态函数进行重构心电信号。从心电信号的非线性动力学角度出发,用多重分形理论进行分析,研究其质量指数曲线、广义分形维数和多重分形谱,提取合适的多重分形特征,用于支持向量机的训练。实验结果表明,用该方法训练测试30次得到的分类准确率平均值为96.09%,单次实验对正常搏动、左束支传导阻滞信号的分类精确率可达97%以上,证明该方法在心电信号分类中的有效性。The rapid classification of ECG signals plays a vital role in the field of cardiac medical diagnosis. In order to reduce the cost of manual identification and improve the accuracy of ECG signal classification. This paper taked normal beat, atrial premature beat, premature ventricular contraction beat, left bundle branch block and right bundle branch block signal as the research objects, decomposed the ECG signals with the ensemble empirical mode decomposition, and combined the correlation coefficient to select the intrinsic mode function to reconstruct the ECG signal. From the perspective of nonlinear dynamics of ECG signals, the multifractal theory was used to analyze the quality index curve, generalized fractal dimension and multifractal spectrum, and appropriate multifractal characteristic parameters were selected for the training of support vector machines. The experimental results showed that the average classification accuracy obtained by 30 training tests with this method is 96. 09%, and the classification accuracy rate of normal beat and left bundle branch block signal in a single experiment can reach more than 97%, which proves the effectiveness of this method in the classification of ECG signals.

关 键 词：心电信号 集合经验模态分解 多重分形理论 支持向量机 

分 类 号：TN911.7[电子电信—通信与信息系统]