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An Efficient EMG Denoising Technique Based on the W-NLM Method

EasyChair Preprint no. 7855

6 pagesDate: April 28, 2022


Electromyography (EMG) signal denoising is critical in the diagnosis of muscle illnesses and several EMG-based mechatronics applications. This work presents an improved EMG denoising method based on the discrete wavelet transform (DWT) and the non-local means (NLM) estimates. The DWT is efficient at reducing high-frequency noise but denoising the lower-frequency components requires larger decomposition levels, making the system computationally burdensome and unsuitable for real-time implementation. Moreover, the frequency thresholding technique in DWT usually results in critical information loss. On the other hand, the NLM-based method is quite efficient in suppressing the noise in low-frequency regions but suffers from a rare-patch effect in the high-frequency region. This results in loss of morphological structure of the signal. To overcome these shortcomings, the proposed approach initially performs the level one DWT of the noisy signal to decompose it into approximation and detail coefficients for lower and high frequency, respectively. Further, the high-frequency noise is removed by implementing NeighShrinkSURE thresholding on the detail coefficients while the NLM estimation is used to diminish the impact of noise in the low-frequency region. As a result, the proposed approach effectively integrates the capabilities of both NLM and DWT while minimizing the computational demand of the system. Experiments are carried out on EMGLAB synthetic signals to confirm the validity of the proposed approach.

Keyphrases: Denoising, Discrete Wavelet Transform, Electromyography, non-local means, Wavelet Thresholding

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Rinku Bhobhriya and Ritu Boora and Manisha Jangra and Priyanka Dalal},
  title = {An Efficient EMG Denoising Technique Based on the W-NLM Method},
  howpublished = {EasyChair Preprint no. 7855},

  year = {EasyChair, 2022}}
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