EMG signal denoising via Bayesian wavelet shrinkage based on GARCH modeling

2009 ◽  
Author(s):  
Maryam Amirmazlaghani ◽  
Hamidreza Amindavar
Keyword(s):  
Signal Denoising ◽  
Wavelet Shrinkage ◽  
Emg Signal ◽  
Garch Modeling ◽  
Bayesian Wavelet

EMG SIGNAL DENOISING USING ADAPTIVE FILTERS THROUGH HYBRID OPTIMIZATION ALGORITHMS

2020 ◽  
pp. 2150009
Author(s):  
Bhattiprolu Nagasirisha ◽  
V. V. K. D. V. Prasad
Keyword(s):  
Adaptive Filters ◽  
Signal To Noise Ratio ◽  
Optimization Algorithms ◽  
Maximum Error ◽  
Recursive Least Squares ◽  
Signal Denoising ◽  
Recording Equipment ◽  
Advantages And Disadvantages ◽  
Emg Signal ◽  
Signal Features

Electromyography (EMG) signal recording equipment is comparatively modern. Still, there are enough restrictions in detection, recording, and characterization of EMG signals because of nonlinearity in the equipment, which leads to noise components. The most commonly affecting artifacts are Power Line Interference (PLI-Noise), Baseline Wander noise (BW-Noise), and Electrocardiogram noise (ECG-Noise). Adaptive filters are advanced and effective solutions for EMG signal denoising, but the improper tuning of filter coefficients leads to noise components in the denoised EMG signal. This defect in adaptive filters triggers or motivates us to optimize the filter coefficients with existing meta-heuristics optimization algorithms. In this paper, Least Mean Squares (LMS) filter and Recursive Least Squares (RLS) adaptive filter coefficients are optimized with a new Hybrid Firefly–Particle Swarm Optimization (HFPSO) by taking the advantages and disadvantages of both the algorithms. Experiments are conducted with the proposed HFPSO and it proved better in EMG signal denoising in terms of the measured parameters like signal-to-noise ratio (SNR) in dB, maximum error (ME), mean square error (MSE), etc. In the second part of the work, the denoised EMG signal features are extracted for the diagnosis of diseases related to myopathy and neuropathy as EMG signal reflects the neuromuscular function and EMG signal examination may contribute to the diagnosis of muscle disorder linked to myopathy and neuropathy.

IMAGE DENOISING BASED ON WAVELET SHRINKAGE USING NEIGHBOR AND LEVEL DEPENDENCY

2009 ◽  
Vol 07 (03) ◽  
pp. 299-311 ◽  
Author(s):  
DONGWOOK CHO ◽  
TIEN D. BUI ◽  
GUANGYI CHEN
Keyword(s):  
Signal To Noise Ratio ◽  
Signal Denoising ◽  
Wavelet Thresholding ◽  
Wavelet Coefficients ◽  
Wavelet Shrinkage ◽  
Minimum Risk ◽  
Shrinkage Methods ◽  
Risk Estimator ◽  
Visual Artifacts ◽  
Unbiased Risk

Since Donoho et al. proposed the wavelet thresholding method for signal denoising, many different denoising approaches have been suggested. In this paper, we present three different wavelet shrinkage methods, namely NeighShrink, NeighSure and NeighLevel. NeighShrink thresholds the wavelet coefficients based on Donoho's universal threshold and the sum of the squares of all the wavelet coefficients within a neighborhood window. NeighSure adopts Stein's unbiased risk estimator (SURE) instead of the universal threshold of NeighShrink so as to obtain the optimal threshold with minimum risk for each subband. NeighLevel uses parent coefficients in a coarser level as well as neighbors in the same subband. We also apply a multiplying factor for the optimal universal threshold in order to get better denoising results. We found that the value of the constant is about the same for different kinds and sizes of images. Experimental results show that our methods give comparatively higher peak signal to noise ratio (PSNR), are much more efficient and have less visual artifacts compared to other methods.

Block Adaptive Bayesian Wavelet Shrinkage for 2D Signal De-noising

2008 ◽  
Author(s):  
Dachun Zhang ◽  
Gang Liu ◽  
Hongbin Li ◽  
Deqiang Chu ◽  
Yuebin Kang
Keyword(s):  
Wavelet Shrinkage ◽  
Bayesian Wavelet

scholarly journals Application of a Novel Wavelet Shrinkage Scheme to Partial Discharge Signal Denoising of Large Generators

2020 ◽  
Vol 10 (6) ◽  
pp. 2162
Author(s):  
Yanan Li ◽  
Zhaohui Li
Keyword(s):  
Partial Discharge ◽  
Selection Method ◽  
Signal Denoising ◽  
Measured Signal ◽  
Wavelet Shrinkage ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Wavelet Selection ◽  
Core Idea ◽  
The One

Partial Discharge (PD) measurements of large generators are extremely affected and hampered by noise, making the denoising of PD signal an inevitable issue. Wavelet shrinkage is the most commonly employed method for PD signal denoising. The appropriate mother wavelet and decomposition level are critically important for the denoising performance. In consideration of the PD signal characteristics of large generators, a novel wavelet shrinkage scheme for PD signal denoising is presented. In the scheme, a scale dependent wavelet selection method is proposed; the core idea is that the optimum wavelet at each scale is selected as the one maximizing the energy ratio of coefficients beside and inside the range formed by the threshold, which correspond to the signal to be reserved and noise to be removed, respectively. In addition, taking into account the influence of mother wavelet at each scale on the decomposition level, an approach for decomposition level determination is put forward based on the energy composition after decomposition at each scale. The application results on the simulated signals with different SNR obtained by combining the various pulses and measured signal on-site show the effectiveness of the proposed scheme. Besides, the denoising results are compared with that of the existing wavelet selection methods and the proposed wavelet selection method shows an obvious advantage.

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