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

林金朝; 李必禄; 李国权; 黄正文; 庞宇

doi:10.11999/JEIT200915

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

doi: 10.11999/JEIT200915

林金朝^{1, 3},
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
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- 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 LIN^{1, 3},
Bilu LI^{1, 3},
Guoquan LI^{1, 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。
- 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.
- ElectroCardioGraphy (ECG) /
- R-wave recognition /
- Ensemble Empirical Mode Decomposition (EEMD) /
- Signal structure analysis

HTML全文

图 1 本文算法流程图

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图 2 原始sel223信号和添加5 dB高斯白噪声的sel223信号

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图 3 Pan-Tomkins算法对$y(n)$的R波检测结果

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图 4 EEMD-ICA算法对$y(n)$的R波检测结果

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图 5 本文算法对$y(n)$的R波检测结果

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图 6 长停顿心电信号片段R波识别效果

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图 7 T波高大心电信号片段R波识别效果

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表 1 本文算法R波识别性能评估

ECG记录	R峰总数	漏检	误检	错检总数	灵敏度(%)	阳性准确率(%)	准确率(%)
sel100	1134	0	3	3	100.00	99.74	99.74
sel103	1048	0	0	0	100.00	100.00	100.00
sel116	1185	0	0	0	100.00	100.00	100.00
sel213	1642	0	1	1	100.00	99.94	99.94
sel221	1247	1	4	5	99.92	99.68	99.60
sel223	1309	3	2	5	99.77	99.85	99.62
sel230	1077	0	0	0	100.00	100.00	100.00
sel301	1351	2	0	2	99.85	100.00	99.85
sel310	2012	3	0	3	99.85	100.00	99.85
sel803	1026	0	0	0	100.00	100.00	100.00
sel853	1113	1	0	1	99.91	100.00	99.91
sel891	1267	0	0	0	100.00	100.00	100.00
合计	15411	10	10	20	99.94	99.94	99.87

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表 2 3种R波识别算法性能对比

R波识别算法	R波总数	漏检	误检	错检总数	灵敏度(%)	阳性准确率(%)	准确率(%)	平均处理时间(s)
Pan-Tomkins算法^[4]	15411	95	25	120	99.38	99.84	99.22	1.7194
EEMD-ICA算法^[17]	15411	144	40	184	99.07	99.74	98.81	76.9896
本文算法	15411	10	10	20	99.94	99.94	99.87	76.9335
带预处理算法^[26]	15411	13	140	153	99.91	99.09	99.02	114.607

下载: 导出CSV

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