[1]卢莉蓉,牛晓东,王鉴,等.基于EMD与IMF分量统计特性的ECG去噪[J].中国医学物理学杂志,2021,38(12):1529-1534.[doi:DOI:10.3969/j.issn.1005-202X.2021.12.013]
 LU Lirong,NIU Xiaodong,WANG Jian,et al.ECG signal denoising based on EMD and statistical characteristics of IMF components[J].Chinese Journal of Medical Physics,2021,38(12):1529-1534.[doi:DOI:10.3969/j.issn.1005-202X.2021.12.013]
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基于EMD与IMF分量统计特性的ECG去噪()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第12期
页码:
1529-1534
栏目:
医学信号处理与医学仪器
出版日期:
2021-12-24

文章信息/Info

Title:
ECG signal denoising based on EMD and statistical characteristics of IMF components
文章编号:
1005-202X(2021)12-1529-06
作者:
卢莉蓉1牛晓东2王鉴3李春燕4
1.长治医学院生物医学工程系, 山西 长治 046000; 2.长治医学院基础医学部, 山西 长治 046000; 3.中北大学信息探测与处理山西省重点实验室, 山西 太原 030051; 4.山西北方机械制造有限责任公司工艺技术研究所, 山西 太原 030051
Author(s):
LU Lirong1 NIU Xiaodong2 WANG Jian3 LI Chunyan4
1. Department of Biomedical Engineering, Changzhi Medical College, Changzhi 046000, China 2. Department of Basic Medicine, Changzhi Medical College, Changzhi 046000, China 3. Key Laboratory of Information Detection and Processing, North University of China, Taiyuan 030051, China 4. Process Technology Research Institute, Shanxi North Machine-Building Co., Ltd, Taiyuan 030051, China
关键词:
心电信号去噪经验模式分解固有模态函数分量
Keywords:
Keywords: electrocardiogram signal denoising empirical mode decomposition intrinsic mode function component
分类号:
R318;TN911.7
DOI:
DOI:10.3969/j.issn.1005-202X.2021.12.013
文献标志码:
A
摘要:
经验模式分解(EMD)域内心电(ECG)信号的去噪,通常为基于QRS特征波经验性识别固有模态函数(IMF)分量并重建ECG信号。由于该方法引入个人误差,因此识别不准确。针对此问题,本文提出利用EMD与IMF分量统计特性对ECG信号进行去噪。本方法首先对含噪ECG信号进行EMD分解得到一系列IMF分量,然后利用IMF分量的统计特性识别IMF分量属性,并采用被识别为ECG信号的IMF分量重建ECG信号。该识别方法基于统计学方法,具有统计学和现实物理意义。将本方法应用于真实ECG信号去噪处理中,结果表明,本方法可有效去除ECG信号基线漂移噪声与肌电干扰噪声,去噪效果优于经验法。
Abstract:
Abstract: The denoising of electrocardiogram (ECG) signal in empirical mode decomposition (EMD) domain usually involves the empirical identification of the intrinsic mode function (IMF) component based on QRS characteristic wave and the reconstruction of ECG signal. However, due to the personal errors caused by empirical method, the identification is inaccurate. To solve this problem, EMD and the statistical characteristics of IMF component are used to denoise ECG signal. Herein a series of IMF components are obtained by EMD on noisy ECG signals, and then the properties of IMF components are identified using the statistical characteristics of IMF components, and the IMF components identified as ECG signals are used to reconstruct ECG signals. The proposed identification method is based on statistical method, with statistical and practical significance. The proposed method is applied to real ECG signal denoising, and the results show that the method can effectively remove the baseline drift noise and electromyography interference noise of ECG signal, achieving a denoising effect better than that of empirical method.

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备注/Memo

备注/Memo:
【收稿日期】2021-06-09 【基金项目】国家自然科学基金(61842103);山西省高等学校科技创新项目(2020L0389) 【作者简介】卢莉蓉,副教授,研究方向:生物医学工程,E-mail: llr1982@163.com 【通信作者】牛晓东,副教授,研究方向:生物信号处理,E-mail: nxd9703@163.com
更新日期/Last Update: 2021-12-24