| 基于集合经验模态分解和信号结构分析的心电信号R波识别算法 |
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| 引用本文： | 林金朝,李必禄,李国权,黄正文,庞宇.基于集合经验模态分解和信号结构分析的心电信号R波识别算法[J].电子与信息学报,2021,43(8):2352-2360. |
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| 作者姓名： | 林金朝 李必禄 李国权 黄正文 庞宇 |
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| 作者单位： | 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 |
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| 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) |
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| 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。
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| 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 |
| 收稿时间： | 2020-10-26 |
| 修稿时间： | 2021-07-21 |
ElectroCardioGram R-wave Recognition Algorithm Based on Ensemble Empirical Mode Decomposition and Signal Structure Analysis |
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| Jinzhao LIN,Bilu LI,Guoquan LI,Zhengwen HUANG,Yu PANG.ElectroCardioGram R-wave Recognition Algorithm Based on Ensemble Empirical Mode Decomposition and Signal Structure Analysis[J].Journal of Electronics & Information Technology,2021,43(8):2352-2360. |
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| Authors: | Jinzhao LIN Bilu LI Guoquan LI Zhengwen HUANG Yu PANG |
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| Affiliation: | 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 |
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| 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. |
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| Keywords: | ElectroCardioGraphy (ECG) R-wave recognition Ensemble Empirical Mode Decomposition (EEMD) Signal structure analysis |
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