李必禄1, 3,
李国权1, 3,,,
黄正文2,
庞宇3
1.重庆邮电大学通信与信息工程学院 重庆 400065
2.布鲁内尔大学电子与计算机工程系 伦敦 UB8 3PH
3.光电信息感测与传输技术重点实验室 重庆 400065
基金项目:国家重点研发计划(2019YFC1511300)，国家自然科学基金(61971079)，重庆市自然科学基金面上项目(cstc2019jcyj-msxmX0666)，四川省区域创新合作项目(2020YFQ0025)，重庆市创新群体(cstc2020jcyj-cxttX0002)，重庆市教委科学技术研究项目(KJZD-K20200604)
详细信息
作者简介:林金朝：男，1966年生，教授，研究方向为无线通信传输技术、医疗信号处理等
李必禄：男，1997年生，硕士，研究方向为医疗信号处理、人工智能
李国权：男，1980年生，教授，研究方向为MIMO无线通信传输技术、医疗信号处理等
黄正文：男，1981年生，讲师/高级研究员，研究方向为人工智能、复杂系统优化、数据分析等
庞宇：男，1978年生，讲师，博士生导师，研究方向为无线通信、集成电路设计、数字医疗研究以及人工智能
通讯作者:李国权 ligq@cqupt.edu.cn
中图分类号:TN911.72; R540.41计量
文章访问数:191
HTML全文浏览量:84
PDF下载量:23
被引次数:0
出版历程
收稿日期:2020-10-26
修回日期:2021-07-21
网络出版日期:2021-07-22
刊出日期:2021-08-10
ElectroCardioGram R-wave Recognition Algorithm Based on Ensemble Empirical Mode Decomposition and Signal Structure Analysis
Jinzhao LIN1, 3,Bilu LI1, 3,
Guoquan LI1, 3,,,
Zhengwen HUANG2,
Yu PANG3
1. School of Communication and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Department of Electronic and Computer Engineering, Brunel University London, London UB8-3PH, UK
3. Key Laboratory of Photoelectric Information Sensing and Transmission Technology, Chongqing 400065, China
Funds:The National Key Research and Development Program (2019yfc1511300), The National Natural Science Foundation of China (61971079)，The General Program of Chongqing Natural Science Foundation (cstc2019jcyj-msxmx0666), The Sichuan Innovation Cooperation Program (2020YFQ0025), The Chongqing Creative Group Program (cstc2020jcyj-cxttX0002), The Science and Technology Research Program of Chongqing Education Committee (KJZD-K20200604)
摘要
摘要:R波作为确定心电信号各波段的重要参考，是心电自动分析的前提。针对大多数R波识别算法的预处理过程影响识别准确度和耗时问题，该文提出一种基于集合经验模态分解(EEMD)和信号结构分析的算法对带噪心电信号(ECG)的R波直接进行识别。首先通过EEMD将带噪声的心电信号分解成一系列本征模态分量，然后对分解后的各模态分量作独立成分分析以提取出R波特征最明显的成分，对该成分进行结构分析，从而实现对R波的准确定位。仿真结果表明，该文算法对带噪声心电信号的R波识别具有更优性能，对异常心电信号的R波识别也具有明显效果。
关键词:心电信号/
R波识别/
集合经验模态分解/
信号结构分析
Abstract:In view of the problem that the preprocessing process of most R-wave recognition algorithms affects the accuracy of recognition and spends more time, an algorithm based on Ensemble Empirical Mode Decomposition (EEMD) and signal structure analysis is proposed to recognize R-wave of ElectroCardioGram (ECG) signals with noise directly. Firstly, the ECG signal with noise is decomposed into a series of intrinsic mode components by EEMD. After that, the intrinsic components are analyzed as independent components to extract the most obvious component of R waves. Finally, the structure of the component is analyzed to realize the accurate positioning of R wave. The simulation results show that the proposed algorithm has better performance in R-wave recognition of noisy ECG signals and demonstrates obvious advantages especially for abnormal ECG signals.
Key words:ElectroCardioGraphy (ECG)/
R-wave recognition/
Ensemble Empirical Mode Decomposition (EEMD)/
Signal structure analysis
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