【摘要】 针对心电信号中肌电干扰噪声难以去除的问题，提出一种基于参数优化变分模态分解（VMD）的信号降噪方法。通过设计动态边界策略和反向种群生成方式，对白鲸优化（BWO）算法进行改进；采用改进白鲸优化算法对VMD参数自适应寻优，确定分解层数K与惩罚因子α；对含噪心电信号进行分解，得到k个本征模态函数（IMF）分量，同时采用相关系数法进行有效模态和含噪模态识别；对噪声主导的模态分量采用小波阈值降噪，并重构信号主导模态与降噪后模态。对仿真信号与含真实肌电干扰的心电信号进行降噪处理，实验结果表明，所提方法去噪效果优于小波阈值去噪法、EMD法、EMD-小波阈值去噪法，真实含噪的心电信号经该方法去噪后自相关系数可达0.91以上。更多还原

【Abstract】 In allusion to the problem of difficult removal of electromyographic interference noise in electrocardiogram(ECG)signals, a method of signal denoising based on parameter-optimized variational mode decomposition(VMD) is proposed. The beluga whale optimization(BWO) algorithm is improved by designing the dynamic boundary strategy and inverse population generation. The improved BWO algorithm is used for the adaptive optimization of the VMD parameters to determine the number of decomposition layers K and the penalty factor α. The noise-containing ECG signal is decomposed to obtain k intrinsic mode function(IMF) components, and the correlation coefficient method is used to identify the effective modes and noise-containing modes. The noise-dominated modal components are noise-reduced by means of the wavelet thresholding, and the dominant modes and noise-reduced modes of the reconstructed signal are reconstructed. The simulation signals and ECG signals with real EMG interference are processed for the denoising. The experimental results show that the proposed method is superior to wavelet threshold denoising method, EMD method and EMD-wavelet threshold denoising method, and the autocorrelation coefficient of real ECG signals with noise can reach more than 0.91 after denoising.更多还原