基于经验小波变换优化自适应混合滤波器的心电信号去噪

doi:10.1007/s12204-023-2591-1

J Shanghai Jiaotong Univ Sci ›› 2025, Vol. 30 ›› Issue (1): 66-80.doi: 10.1007/s12204-023-2591-1

基于经验小波变换优化自适应混合滤波器的心电信号去噪

BALASUBRAMANIAN S1, NARUK Mahaveer Singh2, TEWARI Gaurav3

(1. Department of Electronics and Communication Engineering, Maharishi University of Information Technology, Maharishi Vidya Mandir, Lucknow, 226013, India; 2. Department of Electronics and Communication Engineering, IEC College of Engineering and Technology, Greater Noida, 201310, India; 3. Department of Electrical Communication Engineering, School of Information and Communication Technology, Gautam Buddha University, Greater Noida, 201310, India)

接受日期:2022-11-22 出版日期:2025-01-28 发布日期:2025-01-28

Electrocardiogram Signal Denoising Using Optimized Adaptive Hybrid Filter with Empirical Wavelet Transform

BALASUBRAMANIAN S^1*, NARUKA Mahaveer Singh², TEWARI Gaurav³

(1. Department of Electronics and Communication Engineering, Maharishi University of Information Technology, Maharishi Vidya Mandir, Lucknow 226013, India; 2. Department of Electronics and Communication Engineering, IEC College of Engineering and Technology, Greater Noida 201310, India; 3. Department of Electrical Communication Engineering, School of Information and Communication Technology, Gautam Buddha University, Greater Noida 201310, India)

Accepted:2022-11-22 Online:2025-01-28 Published:2025-01-28

摘要/Abstract

摘要： 心血管疾病是世界上最主要的死亡原因。几十年来，人类心脏的健康一直是一个令人感兴趣的话题。心电图（ECG）信号是判断心脏健康状况的一种综合性的无创方法。许多健康医师利用心电图信号来确定心脏的关键信息。本文将群体智能方法应用于生物医学信号处理领域，以增强自适应混合滤波器和经验小波变换（EWT）。首先对输入心电信号加入高斯白噪声，然后对其进行EWT；采用提出的自适应混合滤波器对ECG信号进行去噪处理。利用蜜獾优化（HBO）算法优化EWT窗函数和自适应混合滤波器权重参数。所提方法在MATLAB 2018a中使用MIT-BIH数据集进行仿真，该数据集包含高斯白噪声、肌电图噪声和电极运动伪影噪声。与基于递归最小二乘的自适应滤波器、多通道最小均方方法和离散小波变换方法进行比较，验证了HBO方法的有效性。实验结果表明，在EWT和自适应混合滤波的支持下，HBO方法可以有效应用于心血管信号去噪。

关键词: 心电图（ECG）信号去噪，经验小波变换（EWT），蜜獾优化（HBO），自适应混合滤波器，窗函数

Abstract: Cardiovascular diseases are the world’s leading cause of death; therefore cardiac health of the human heart has been a fascinating topic for decades. The electrocardiogram (ECG) signal is a comprehensive noninvasive method for determining cardiac health. Various health practitioners use the ECG signal to ascertain critical information about the human heart. In this article, swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms (EWTs). At first, the white Gaussian noise is added to the input ECG signal and then applied to the EWT. The ECG signals are denoised by the proposed adaptive hybrid filter. The honey badge optimization (HBO) algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters. The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian, electromyogram and electrode motion artifact noises. A comparison of the HBO approach with recursive least square-based adaptive filter, multichannel least means square, and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter. The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising.

Key words: electrocardiogram (ECG) signal denoising, empirical wavelet transform (EWT), honey badge optimization (HBO), adaptive hybrid filter, window function

中图分类号:

BALASUBRAMANIAN S1, NARUK Mahaveer Singh2, TEWARI Gaurav3. 基于经验小波变换优化自适应混合滤波器的心电信号去噪[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 66-80.

BALASUBRAMANIAN S^1*, NARUKA Mahaveer Singh², TEWARI Gaurav³. Electrocardiogram Signal Denoising Using Optimized Adaptive Hybrid Filter with Empirical Wavelet Transform[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 66-80.

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基于经验小波变换优化自适应混合滤波器的心电信号去噪

Electrocardiogram Signal Denoising Using Optimized Adaptive Hybrid Filter with Empirical Wavelet Transform

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