scholarly journals Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains

2014 ◽  
Vol 1 (3) ◽  
pp. 104-109 ◽  
Author(s):  
Salim Lahmiri
Keyword(s):  
Comparative Study ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Wavelet Thresholding ◽  
Variational Mode Decomposition ◽  
Mode Decomposition

A New Denoising Method via Empirical Mode Decomposition

2014 ◽  
Vol 651-653 ◽  
pp. 2090-2093 ◽  
Author(s):  
Shou Cheng Zhang ◽  
Li Li Sui
Keyword(s):  
Empirical Mode Decomposition ◽  
Basic Principle ◽  
Random Noise ◽  
Doppler Signal ◽  
Threshold Function ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Wavelet Thresholding ◽  
Denoising Method ◽  
Mode Decomposition

In non-parametric signal denoising area, empirical mode decomposition is potentially useful. In this paper, the wavelet thresholding principle is directly used in EMD-based denoising. The basic principle of the method is to reconstruct the signal with IMFs previously thresholded. A novel threshold function is proposed to improve denoising effect by exploiting the special characteristics of the hard and soft thresholding method. The denoising method is validated through experiments on the “Doppler” signal and a real ECG signal from MIT-BIH databases corrupted by additive white Gaussian random noise. The simulations show that the proposed EMD-based method provides very good results for denoising.

scholarly journals Dictionary adaptation and variational mode decomposition for gyroscope signal enhancement

2020 ◽  
Author(s):  
Krzysztof Brzostowski ◽  
Jerzy Świa̧tek
Keyword(s):  
Great Improvement ◽  
Augmented Lagrangian ◽  
Optimization Method ◽  
Future Research ◽  
Signal Denoising ◽  
Variational Mode Decomposition ◽  
Signal Degradation ◽  
Mode Decomposition ◽  
Almost All ◽  
Different Levels

Abstract The paper proposes an approach to signal denoising based on a combination of Variational Mode Decomposition with the Split Augmented Lagrangian Shrinkage Algorithm. In our research, we found that the proposed approach gives a great improvement of denoising gyroscopic signals. In turn, the results for the synthetic signals are not straightforward. For the bumps synthetic signals, the proposed algorithm gives the best results for different levels of signal degradation. While for the Doppler and blocks synthetic signals the reference methods give better results. However, for heavisine test signal the proposed algorithm gives better results in almost all cases. A weak point of the presented algorithm is its time complexity. The proposed approach is based on the Split Augmented Lagrangian Shrinkage Algorithm, which is the iterative optimization method since the time of computation strongly depends on the number of iterations. The presented results show that the proposed approach gives a great improvement in signal denoising and it is a promising direction of future research.

Application of improved variational mode decomposition method based on two-dimensional sparrow search algorithm in natural gas pipeline leakage signal denoising

2021 ◽  
pp. 014233122110636
Author(s):  
Dongmei Wang ◽  
Lijuan Zhu ◽  
Jikang Yue ◽  
Jingyi Lu ◽  
Gongfa Li
Keyword(s):  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Decomposition Algorithm ◽  
Signal Denoising ◽  
Two Dimensional ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Whale Optimization

To eliminate noise interference in pipeline leakage detection, a signal denoising method based on an improved variational mode decomposition algorithm is proposed. This work adopts a standard variational mode decomposition algorithm with decomposition level K and the penalty factor α. The improvements consist of using a two-dimensional sparrow search algorithm to find K and α. To verify the superiority of the sparrow search algorithm to find K and α, it is compared with three earlier studies. These studies used the firefly algorithm, particle swarm optimization, and whale optimization algorithm to perform the optimization. The main result of this study is to demonstrate that the variational mode decomposition improved by sparrow search algorithm gives a much improved signal-to-noise ratio compared to the other methods. In all other respects, the results are comparable.

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