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Extraction of the EPP Component from the Surface EMG
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EWT-IIT: a surface electromyography denoising method.

Feiyun Xiao1,2,3

  • 1School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, China. xfymusic@163.com.

Medical & Biological Engineering & Computing
|October 10, 2022
PubMed
Summary
This summary is machine-generated.

Noise significantly impacts surface electromyography (sEMG) research. A new EWT-IIT algorithm effectively denoises sEMG signals, improving accuracy for applications like motion recognition and diagnostics.

Keywords:
DenoisingEmpirical wavelet transformInterval thresholdingsEMG

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Area of Science:

  • Biomedical Engineering
  • Signal Processing

Background:

  • Surface electromyography (sEMG) signals are crucial for applications like motion intention recognition, disease diagnosis, and human-computer interaction.
  • Noise interference in sEMG signals significantly degrades the quality and reliability of subsequent analyses.

Purpose of the Study:

  • To propose and evaluate a novel algorithm for denoising surface electromyography (sEMG) signals.
  • To enhance the effectiveness of sEMG signal processing for improved research outcomes.

Main Methods:

  • An sEMG denoising algorithm termed EWT-IIT, combining Empirical Wavelet Transform (EWT) and Improved Interval Thresholding (IIT).
  • EWT decomposes noisy sEMG signals into empirical intrinsic modal functions (EIMFs).
  • IIT is applied to each EIMF for noise reduction, leveraging advantages of both hard and soft thresholding functions.

Main Results:

  • The proposed EWT-IIT method demonstrated effective noise removal from sEMG signals.
  • Quantitative evaluation using Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE) confirmed the denoising efficacy.
  • Simulated and experimental results indicated superior denoising performance compared to existing methods.

Conclusions:

  • The EWT-IIT algorithm provides a robust and effective solution for sEMG signal denoising.
  • This method enhances signal quality, paving the way for more accurate sEMG-based applications.
  • The developed IIT function offers an improved thresholding approach for signal processing challenges.