基于能量选择可调Q因子小波变换和改进SVD的动态心电图混合噪声抑制方法研究

杨欣卢; 王文波; 邢远秀; 邓钊

doi:10.12263/DZXB.20250893

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基于能量选择可调Q因子小波变换和改进SVD的动态心电图混合噪声抑制方法研究

学术论文 | 更新时间：2026-04-24

- 基于能量选择可调Q因子小波变换和改进SVD的动态心电图混合噪声抑制方法研究
- Hybrid Noise Reduction in Dynamic Electrocardiograms via Tunable Q-Factor Wavelet Transform with Energy Selection of Subband and Improved Singular Value Decomposition
- 电子学报 2025年53卷第12期页码：4640-4655
- 作者机构：
  
  1.武汉科技大学数学与系统科学学院，湖北武汉 430081
  2.冶金工业过程系统科学湖北省重点实验室，湖北武汉 430081
- 作者简介：
  
  [ "杨欣卢女，1999年4月出生于湖北省荆州市.现为武汉科技大学数学与系统科学学院博士研究生.主要研究方向为统计分析和信号处理.E-mail: yangxinlu99@qq.com" ]
  [ "王文波男，1978年5月出生于湖北省襄阳市.现为武汉科技大学数学与系统科学学院教授、博士生导师.获湖北省自然科学奖、全国发明协会发明创新奖等奖项4项.在国内外发表学术论文70余篇.主要研究方向为信号处理与深度学习.E-mail: wangwenbo@wust.edu.cn" ]
  [ "邢远秀女，1980年8月出生于河南省南阳市.2002年毕业于武汉理工大学计算机科学与技术系.现为武汉科技大学数学与系统科学学院副教授.主要研究方向为图像处理与深度学习.E-mail: xinyuanxiu@wust.edu.cn" ]
  [ "邓钊男，1991年10月出生于湖北省武汉市.现为武汉科技大学数学与系统科学学院副教授.主要研究方向为最优化理论与方法.E-mail: dengzhao@wust.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(12501435)
- DOI：10.12263/DZXB.20250893
  中图分类号： TN911.72;
- 收稿：2025-12-05，
  
  录用：2025-12-11，
  
  纸质出版：2025-12-25
- 稿件说明：
移动端阅览
杨欣卢, 王文波, 邢远秀, 等. 基于能量选择可调Q因子小波变换和改进SVD的动态心电图混合噪声抑制方法研究[J]. 电子学报, 2025, 53(12): 4640-4655.

YANG Xin-lu, WANG Wen-bo, XING Yuan-xiu, et al. Hybrid Noise Reduction in Dynamic Electrocardiograms via Tunable Q-Factor Wavelet Transform with Energy Selection of Subband and Improved Singular Value Decomposition[J]. Acta Electronica Sinica, 2025, 53(12): 4640-4655.
杨欣卢, 王文波, 邢远秀, 等. 基于能量选择可调Q因子小波变换和改进SVD的动态心电图混合噪声抑制方法研究[J]. 电子学报, 2025, 53(12): 4640-4655. DOI：10.12263/DZXB.20250893

YANG Xin-lu, WANG Wen-bo, XING Yuan-xiu, et al. Hybrid Noise Reduction in Dynamic Electrocardiograms via Tunable Q-Factor Wavelet Transform with Energy Selection of Subband and Improved Singular Value Decomposition[J]. Acta Electronica Sinica, 2025, 53(12): 4640-4655. DOI：10.12263/DZXB.20250893

摘要

动态心电图（ElectroCardioGram，ECG）在临床监测与可穿戴健康评估中具有重要应用价值，但其信号幅值低、非平稳性强，在实际采集过程中易同时受到基线漂移（Baseline Wander，BW）、肌电伪迹（Muscle Artifact，MA）、电极运动（Electrode Motion，EM）及环境噪声（如高斯白噪声（White Gaussian Noise，WGN））等多源干扰的叠加污染，导致关键波形特征（P波、QRS波群与T波）失真，严重制约可穿戴设备自动分析与临床判读的可靠性.此外，现有ECG去噪方法多针对单一噪声类型或理想工况设计，在多源混合噪声及低信噪比条件下，仍面临噪声抑制不足与形态保真难以兼顾的问题.针对上述挑战，本文提出一种联合能量选择可调Q因子小波变换和改进奇异值分解的二次降噪方法（Energy-Selected Tunable Q-factor Wavelet Transform with Improved Singular Value Decomposition，ES-TQWT-ISVD）.该方法首先利用TQWT的多分辨率分析能力，将含噪ECG信号分解为多个具有不同振荡特性的子带分量.在此基础上，依据混合噪声在时频域中的能量分布差异，构建子带能量占比与累计能量判据，自适应筛选信号主导子带，实现对噪声成分的初步抑制.随后，将筛选后的子带信号构造Hankel矩阵，并引入一种基于奇异值子集标准差突变检测的自适应定阶策略，以确定最优重构阶数，从而在无需经验阈值的条件下进一步削弱残余噪声并保持波形细节.基于MIT-BIH心律失常数据库与MIT-BIH噪声应力测试数据库构建的四种单一噪声（WGN、BW、MA、EM）及四种混合噪声（BW+MA、BW+EM、EM+MA、BW+MA+EM）实验，对所提方法在不同噪声强度和噪声组合条件下的去噪性能进行了系统评估.实验结果表明，在-5 dB的强噪声环境下，所提方法仍可实现12.46 dB的信噪比提升，同时保持较低的均方根误差（0.057）和较高的余弦相似度（91.07%），在噪声抑制效果与心电波形保持性方面均优于传统TQWT、自适应噪声完全集合经验模态分解（Complete Ensemble Empirical Mode Decomposition with Adaptive Noise，CEEMDAN）方法，并在多源混合噪声场景下表现出良好的综合性能.研究结果表明，该方法无需训练样本，计算复杂度适中，在特征波定位任务中表现出较高的检测一致性，适用于复杂动态环境下ECG信号的高质量去噪与临床前端处理.

Abstract

Dynamic electrocardiograms (ECGs) play an important role in clinical monitoring and wearable health assessment. However

due to their low amplitude and strong nonstationarity

ECG signals are highly susceptible to contamination by multiple sources of interference during acquisition

including baseline wander (BW)

muscle artifacts (MA)

electrode motion (EM)

and environmental noise such as white Gaussian noise (WGN). The superposition of these disturbances leads to distortion of critical waveform components (P wave

QRS complex

and T wave)

severely limiting the reliability of automatic analysis and clinical interpretation in wearable devices. Moreover

most existing ECG denoising methods are designed for single noise types or ideal operating conditions

and they often fail to simultaneously achieve effective noise suppression and waveform fidelity under multi-source mixed-noise and low signal-to-noise ratio (SNR) conditions. To address these challenges

a two-stage denoising method that combines an energy-selected tunable Q-factor wavelet transform with improved singular value decomposition (ES-TQWT-ISVD) is proposed. First

the multiresolution analysis capability of TQWT is employed to decompose noisy ECG signals into multiple subband components with different oscillatory characteristics. Based on the energy distribution differences of mixed noise in the time-frequency domain

criteria based on subband energy ratios and cumulative energy are constructed to adaptively select signal-dominant subbands

thereby achieving preliminary noise suppression. Subsequently

the selected subband signals are used to construct a Hankel matrix

and an adaptive order-determination strategy based on abrupt changes in the standard deviation of singular value subsets is introduced to identify the optimal reconstruction order. In this way

residual noise is further attenuated without relying on empirical thresholds

while preserving fine waveform details. Experiments were conducted on four types of single noise (WGN

and EM) and four types of mixed noise (BW+MA

BW+EM

EM+MA

and BW+MA+EM)

constructed using the MIT-BIH Arrhythmia Database and the MIT-BIH Noise Stress Test Database

to systematically evaluate the denoising performance of the proposed method under different noise intensities and combinations. The experimental results demonstrate that

even under severe noise conditions at -5 dB

the proposed method achieves an SNR improvement of 12.46 dB

while maintaining a low root mean square error (0.057) and a high cosine similarity (91.07%). Compared with conventional TQWT and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) based methods

the proposed approach exhibits superior noise suppression capability and waveform preservation performance

and shows robust overall performance in multi-source mixed-noise scenarios. The results further indicate that the proposed method does not require training samples

has moderate computational complexity

and exhibits high detection consistency in feature wave localization tasks

making it suitable for high-quality ECG denoising and clinical front-end processing in complex dynamic environments.

关键词

Keywords

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