Mao Jiandong, Li Zhiyuan, Li Shun, Li Juan
School of Electrical and Information Engineering, North Minzu University, North Wenchang Road, Yinchuan 750021, China.
Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia Province, North Wenchang Road, Yinchuan 750021, China.
Entropy (Basel). 2023 May 10;25(5):775. doi: 10.3390/e25050775.
ECG signal processing is an important basis for the prevention and diagnosis of cardiovascular diseases; however, the signal is susceptible to noise interference mixed with equipment, environmental influences, and transmission processes. In this paper, an efficient denoising method based on the variational modal decomposition (VMD) algorithm combined with and optimized by the sparrow search algorithm (SSA) and singular value decomposition (SVD) algorithm, named VMD-SSA-SVD, is proposed for the first time and applied to the noise reduction of ECG signals. SSA is used to find the optimal combination of parameters of VMD [K,α], VMD-SSA decomposes the signal to obtain finite modal components, and the components containing baseline drift are eliminated by the mean value criterion. Then, the effective modalities are obtained in the remaining components using the mutual relation number method, and each effective modal is processed by SVD noise reduction and reconstructed separately to finally obtain a clean ECG signal. In order to verify the effectiveness, the methods proposed are compared and analyzed with wavelet packet decomposition, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm. The results show that the noise reduction effect of the VMD-SSA-SVD algorithm proposed is the most significant, and that it can suppress the noise and remove the baseline drift interference at the same time, and effectively retain the morphological characteristics of the ECG signals.
心电图信号处理是心血管疾病预防和诊断的重要基础；然而，该信号容易受到与设备、环境影响及传输过程中混合的噪声干扰。本文首次提出了一种基于变分模态分解（VMD）算法，并结合麻雀搜索算法（SSA）和奇异值分解（SVD）算法进行优化的高效去噪方法，即VMD - SSA - SVD，并将其应用于心电图信号的降噪处理。利用SSA寻找VMD参数[K,α]的最优组合，VMD - SSA对信号进行分解以获得有限个模态分量，通过均值准则消除包含基线漂移的分量。然后，利用互相关系数法在剩余分量中获取有效模态，对每个有效模态分别进行SVD降噪和重构，最终得到干净的心电图信号。为验证有效性，将所提方法与小波包分解、经验模态分解（EMD）、集合经验模态分解（EEMD）以及自适应噪声完备总体经验模态分解（CEEMDAN）算法进行比较分析。结果表明，所提VMD - SSA - SVD算法的降噪效果最为显著，能够同时抑制噪声并去除基线漂移干扰，有效保留心电图信号的形态特征。
