Abstract
Heart sound signals are easy to introduce noise during the acquisition process, and traditional denoising algorithms always remove the characteristic information of the heart sound while removing the noise. The denoising effects in turn affect the subsequent diagnosis results. So an improved algorithm based on variational mode decomposition (VMD) and wavelet threshold method is proposed. First, the number of decomposition modes K of the VMD is selected by analyzing the average instantaneous frequency curve of the different decomposition values, and the noisy heart sound is decomposed into K modes by the VMD algorithm. Then, the modes that need to be retained are decided by the energy curve of each mode. Finally, wavelet threshold denoising method is performed on the retained modes. Experiment simulation results show that under different signal-to-noise ratio conditions, the proposed method can improve heart sounds’ ratio of signal to noise and reduce the root mean square error. Compared with traditional algorithms, it has good noise suppression capabilities under different noise levels.
Highlights
The heart sound is a bioelectric signal collected under a strong noise background
The number of decomposition modes K of the variational mode decomposition (VMD) is selected by analyzing the average instantaneous frequency curve of the different decomposition values, and the noisy heart sound is decomposed into K modes by the VMD algorithm
The empirical mode decomposition (EMD) algorithm [6] can decompose heart sounds into a series of intrinsic mode functions (IMFs), but it is prone to mode mixing
Summary
The heart sound is a bioelectric signal collected under a strong noise background. The noise will influence the correct diagnosis and recognition of the heart sound. [2] [3] [4] is often used to eliminate noise components of heart sounds, but the choice of threshold function [5] affects the denoising effect. The empirical mode decomposition (EMD) algorithm [6] can decompose heart sounds into a series of intrinsic mode functions (IMFs), but it is prone to mode mixing. Aiming at the shortcomings of EMD, the ensemble empirical mode decomposition (EEMD) algorithm [7] is proposed and it can solve the problem of mode mixing by adding noise. The VMD [9] algorithm was proposed in 2014 This method can realize the adaptive decomposition of each mode, which overcomes the mode mixing problem of EMD. The retained heart sound is denoised by the adaptive threshold wavelet transform
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