电子学报2025，Vol.53Issue(12)：4640-4655,16.DOI:10.12263/DZXB.20250893
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
摘要
Abstract
Dynamic electrocardiograms(ECGs)play an important role in clinical monitoring and wearable health as⁃sessment.However,due to their low amplitude and strong nonstationarity,ECG signals are highly susceptible to contamina⁃tion by multiple sources of interference during acquisition,including baseline wander(BW),muscle artifacts(MA),elec⁃trode 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 de⁃signed for single noise types or ideal operating conditions,and they often fail to simultaneously achieve effective noise sup⁃pression 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 character⁃istics.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 pre⁃liminary noise suppression.Subsequently,the selected subband signals are used to construct a Hankel matrix,and an adap⁃tive 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,BW,MA,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 per⁃formance 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 pro⁃posed 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
可调Q因子小波变换/心电信号降噪/奇异值分解/混合噪声抑制/特征波定位Key words
tunable Q-factor wavelet transform/electrocardiograms signal denoising/singular value decomposition/mixed noise suppression/feature wave localization分类
信息技术与安全科学引用本文复制引用
杨欣卢,王文波,邢远秀,邓钊..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):4640-4655,16.基金项目
国家自然科学基金(No.12501435) National Natural Science Foundation of China(No.12501435) （No.12501435）
