采用集合经验模态分解和改进阈值函数的心电自适应去噪方法

尹丽; 陈富民; 张琦; 陈鑫

doi:10.7652/xjtuxb202001013

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采用集合经验模态分解和改进阈值函数的心电自适应去噪方法

更新时间：2025-07-09

- 采用集合经验模态分解和改进阈值函数的心电自适应去噪方法
- ECG Adaptive Denoising Method Based on EEMD and Improved Threshold Function
- 西安交通大学学报 2020年54卷第1期页码：101-107
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202001013
  中图分类号： R318;TN911
- 网络首发：2020-01-10，
  
  纸质出版：2020
- 稿件说明：
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尹丽, 陈富民, 张琦, 等. 采用集合经验模态分解和改进阈值函数的心电自适应去噪方法[J]. 西安交通大学学报, 2020,54(1):101-107.

ECG Adaptive Denoising Method Based on EEMD and Improved Threshold Function[J]. 2020, 54(1): 101-107.
尹丽, 陈富民, 张琦, 等. 采用集合经验模态分解和改进阈值函数的心电自适应去噪方法[J]. 西安交通大学学报, 2020,54(1):101-107. DOI： 10.7652/xjtuxb202001013.

ECG Adaptive Denoising Method Based on EEMD and Improved Threshold Function[J]. 2020, 54(1): 101-107. DOI： 10.7652/xjtuxb202001013.

摘要

针对心电信号中存在基线漂移、工频和肌电干扰等噪声对后续的分析和诊断带来干扰的问题

提出了集合经验模态分解(EEMD)改进阈值函数的心电自适应去噪方法。运用EEMD将含噪心电信号分解得到一组由高频到低频分布的固有模态函数(IMF)。采用过零率自适应判断各IMF的噪声类别:若IMF包含高频噪声

采用结合软硬阈值优缺点所提出的改进阈值函数以去除IMF分量中的高频噪声; 若IMF包含低频的基线漂移

则采用中值滤波器抑制基线漂移。最后将处理后的IMF分量叠加

即可重构去噪后的心电信号。实验结果表明

与已有的小波阈值法去噪后的信噪比(SNR)和均方根误差(RSME)对比

所提方法对心电信号去噪效果更加显著

而且能完整地保留波形特征。

Abstract

Aiming at the problem that the noise such as baseline drift

power frequency and myoelectric interference in the ECG signal interferes with subsequent signal analysis and diagnosis

an ECG adaptive denoising method with ensemble empirical mode decomposition(EEMD)and improved threshold function is proposed. The noisy ECG signal is firstly decomposed into a set of intrinsic mode functions(IMFs)by EEMD. The IMFs are distributed from high frequency to low frequency. Then

-ero-crossing rate is used as a criterion to adaptively determine the noise category of each IMF. If the IMF contains high frequency noise

an improved threshold function in combination with the advantages and disadvantages of the soft and hard threshold functions is proposed to remove high frequency noise in the IMF component. If the IMF includes a low-frequency baseline drift

the median filter is chosen to suppress the baseline drift. Finally

all the processed IMFs are superimposed to reconstruct the de-noised ECG sig

关键词

Keywords

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