Research on Wavelet Denoising for Pulse Signal Based on Improved Wavelet Thresholding

Abstract

Pulse signal is the non-stationary random signal, the signal denoising is an important task before analyzing it. Based on wavelet thresholding denoising method presented by Donoho, a new compromising threshold function is proposed. Compared with classical thresholding denoising methods, it overcomes the discontinuity of the hard-thresholding method and reduces the fixed deviation between the estimated wavelet coefficients and the decomposed wavelet coefficients of the softthresholding method. The experiment results show that the improved method gives better Signal to Noise Ratio (SNR) and Mean Square Error (MSE) than the soft-thresholding method, hard-thresholding method and the standard compromising method between them.

A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds

“…The noise threshold for the selected Detail components is obtained either using the universal threshold [31] $\lambda ={\sigma }_j^{\stackrel{true\sim }{\mathrm{italicNoise}}}\sqrt{2logfalse(Nfalse)}$, or decomposition level dependent thresholds ${\lambda }_j={\sigma }_j^{\stackrel{true\sim }{\mathrm{italicNoise}}}\sqrt{2{log}_2{N}_j}$ [32] or ${\lambda }_j=1\frac{{\sigma }_j^{\stackrel{\sim }{\mathit{Noise}}}\sqrt{2log{N}_j}}{log(j+1)}$ [33], where N j is the length of the j th Detail component, and ${\sigma }_j^{\stackrel{true\sim }{\mathrm{italicNoise}}}$ is an estimate of noise level [31]–[33]. However, state-of-the-art level-dependent noise threshold selection methods like Stein’s Unbiased Risk Estimate (SURE) threshold [34], [35] and Minmax threshold [31] are more widely used for their better performance.…”

Ultrasonic Signal De-noising Based on Wavelet Entropy and Inter-Scale Correlation

“…Specifically, the SDR can be described as follows: The test signal can be described as follows: In the first simulation experiment, we compare the output SNR and SDR results of the proposed method with those of wavelet transform using stationary model (WT-SM) [20], wavelet transform using translation invariant model (WT-TIM) [21], and the method proposed by W. X., Ren et al, [13]. The parameters of the test signal are set as f .…”

One Method for Preprocessing Pulse Signal Based on Deep Learning

“…Most of the existing pulse signal preprocessing methods use the lter to remove the noise [1][2][3][4][5][6] and baseline drift [1,2,7,8] coupled into the pulse signal. Regardless of noise or baseline drift, for pure signals, it is generalized noise.…”