两步式自适应阈值法滤除心电信号中运动伪迹

吕建行; 李玉榕; 陈建国; 高宁

doi:10.12263/DZXB.20230886

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两步式自适应阈值法滤除心电信号中运动伪迹

学术论文 | 更新时间：2026-05-07

- 两步式自适应阈值法滤除心电信号中运动伪迹
- ECG Motion Artifact Filtering Based on Two-Stage Adaptive Threshold Rules
- 电子学报 2024年52卷第10期页码：3493-3506
- 作者机构：
  
  1.福州大学电气工程与自动化学院，福建福州 350108
  2.福建省医疗器械和医药技术重点实验室，福建福州 350108
- 作者简介：
  
  [ "吕建行男，1995年3月出生，福建莆田人，福州大学电气工程与自动化学院硕士研究生，主要研究方向为心电信号降噪与智能识别技术.E-mail: 1129153613@qq.com" ]
  [ "李玉榕女，1973年2月出生，福建莆田人，福州大学电气工程与自动化学院教授、博士生导师，主要研究方向为多模态电生理信号建模与智能康复技术研究与应用.E-mail: liyurong@fzu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62373108)
- DOI：10.12263/DZXB.20230886
  中图分类号： TN911.7;TP274
- 收稿：2023-09-20，
  
  修回：2024-02-22，
  
  纸质出版：2024-10-25
- 稿件说明：
移动端阅览
吕建行, 李玉榕, 陈建国, 等. 两步式自适应阈值法滤除心电信号中运动伪迹[J]. 电子学报, 2024, 52(10): 3493-3506.

LÜ Jian-hang, LI Yu-rong, CHEN Jian-guo, et al. ECG Motion Artifact Filtering Based on Two-Stage Adaptive Threshold Rules[J]. Acta Electronica Sinica, 2024, 52(10): 3493-3506.
吕建行, 李玉榕, 陈建国, 等. 两步式自适应阈值法滤除心电信号中运动伪迹[J]. 电子学报, 2024, 52(10): 3493-3506. DOI：10.12263/DZXB.20230886

LÜ Jian-hang, LI Yu-rong, CHEN Jian-guo, et al. ECG Motion Artifact Filtering Based on Two-Stage Adaptive Threshold Rules[J]. Acta Electronica Sinica, 2024, 52(10): 3493-3506. DOI：10.12263/DZXB.20230886

摘要

心电信号广泛应用于心脏疾病的医学检测中，可穿戴动态心电监测设备可以实现对心律失常的风险识别并预警.相比于静息心电信号，动态心电信号在采集过程中会受到更大运动伪迹的干扰，这些干扰会覆盖心电信号的关键信息，限制其临床应用.本文兼顾心电信号局部和全局特征，利用其周期性，研究了一种将心电信号低频PT波和高频QRS波群分开处理的两步式自适应阈值滤波算法，适用于单通道心电信号中的运动伪迹滤除.第一步先通过多分辨率阈值初步抑制心电信号低频部分中的运动伪迹；第二步，对受运动伪迹影响而不平衡的QRS波进行自适应阈值修复，通过对QRS波形调节，减少心电信号中高频部分运动伪迹，同时设置自适应阈值对心电信号P波、T波对应的小波系数进行处理，超出自适应阈值范围的小波系数通过波形缩放进行调整，进一步抑制低频运动伪迹.研究通过不同心电数据库评估算法的性能.在输入信噪比从-10 ~10 dB时，心电信号信噪比提升了10.912 2 dB和4.391 2 dB，滤波后心电信号与纯净心电信号的相关系数分别为0.687 6和0.978 3，提取的运动伪迹与原运动伪迹相关系数分别为0.953 0和0.852 9.实验结果表明，算法在不同噪声水平下，利用自适应阈值的优点，能有效复原受运动伪迹污染的心电信号波形特征，最大限度保留心电信号的临床信息，可作为可穿戴心电设备滤除运动伪迹的有效工具.

Abstract

ECG (ElectroCardioGram) signals are widely used in the medical detection of heart disease

and wearable dynamic ECG monitoring devices enable the detection and early warning of cardiac arrhythmias. Compared to resting ECG signals

dynamic ECG signals are more susceptible to interference from motion artifacts during the data acquisition process. These motion artifacts can obscure critical information within the ECG signal

limiting its clinical utility. In this paper

taking into account the local and global characteristics of the ECG signal and using its periodicity

a two-stage adaptive threshold filtering algorithm is investigated to process the low-frequency PT wave and the high-frequency QRS wave group separately

which is suitable for motion artifact filtering in single-channel ECG signal. In the first step

motion artifacts in the low-frequency part of the ECG signal are suppressed by a multi-resolution threshold. In the second step

the imbalanced QRS wave affected by motion artifacts is repaired by adaptive threshold

adjusting the QRS waveform to reduce motion artifacts in the high-frequency portion of the ECG signal

while setting adaptive thresholds to process the wavelet coefficients corresponding to the P-wave and T-wave of the ECG signal. Wavelet coefficients beyond the adaptive threshold range are adjusted via waveform scaling to further suppress the low-frequency motion artifacts. In this paper

the performance of the algorithm is evaluated using different ECG databases. When the input SNR changes from -10~10 dB

the SNR of the ECG signal increases by 10.912 2 dB and 4.391 2 dB

respectively

and the correlation coefficients between the filtered ECG signal and the pure ECG signal are 0.687 6 and 0.978 3

respectively

the correlation coefficients between the extracted motion artifacts and the original motion artifacts are 0.953 0 and 0.852 9

respectively. The experimental results show that under different noise levels

the proposed algorithm can effectively recover the ECG waveform characteristics contaminated by motion artifacts by exploiting the advantages of adaptive threshold

and retain the clinical information of ECG signals to the maximum extent

and can be used as an effective tool for filtering motion artifacts in wearable ECG devices.

关键词

Keywords

references

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