Key result
The proposed VMD-SSA-SVD algorithm significantly suppressed noise and removed baseline drift interference while effectively retaining the morphological characteristics of ECG signals.
Why the study?
ECG signals are susceptible to noise interference from equipment, environmental influences, and transmission processes.
Does the VMD-SSA-SVD algorithm improve noise reduction in ECG signals compared to traditional decomposition algorithms?
Does the VMD-SSA-SVD algorithm improve noise reduction in ECG signals compared to traditional decomposition algorithms?
The novel VMD-SSA-SVD algorithm provides superior ECG signal denoising and baseline drift removal compared to traditional decomposition methods, potentially improving the basis for cardiovascular disease diagnosis.
May enhance ECG denoising in research settings; leaves open clinical validation before diagnostic impact.
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.
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Mao et al. (2023) studied Cardiovascular diseases (ECG signal processing). VMD-SSA-SVD algorithm vs. Wavelet packet decomposition, EMD, EEMD, and CEEMDAN algorithms was evaluated on Noise reduction effect and baseline drift removal. The proposed VMD-SSA-SVD algorithm significantly suppressed noise and removed baseline drift interference while effectively retaining the morphological characteristics of ECG signals.
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