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Denoising of surface electromyogram based on complementary ensemble empirical mode decomposition and improved

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This study introduces a novel method using complementary ensemble empirical mode decomposition (CEEMD) and improved interval thresholding (IT) to effectively denoise surface electromyography (sEMG) signals. The technique enhances signal quality for improved hand motion recognition.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Physiology

Background:

  • Surface electromyogram (sEMG) signals are crucial physiological indicators but are susceptible to noise, compromising data integrity and application accuracy.
  • Effective noise reduction is essential for reliable analysis and application of sEMG signals in fields like biomechanics and neurorehabilitation.

Purpose of the Study:

  • To develop and evaluate a novel noise reduction scheme for sEMG signals.
  • To improve the signal-to-noise ratio (SNR) and reduce root-mean-square error (RMSE) of sEMG signals.
  • To assess the efficacy of the denoising method in a hand motion recognition task.

Main Methods:

  • A novel denoising scheme combining complementary ensemble empirical mode decomposition (CEEMD), improved interval thresholding (IT), and component correlation analysis was proposed.
  • sEMG signals were decomposed into intrinsic mode functions (IMFs) using CEEMD, with relevant IMFs selected via component correlation analysis.
  • Selected IMFs were processed using improved IT, and the sEMG signal was reconstructed from processed and residual IMFs.

Main Results:

  • The proposed method demonstrated significant improvements in SNR (at least 1 dB increase) and RMSE reduction compared to stationary wavelet transform and other empirical mode decomposition-based denoising algorithms.
  • Evaluation using a standard sEMG database showed effective noise reduction across varying SNR levels (1 dB to 25 dB).
  • Application to hand motion recognition revealed a higher recognition rate for denoised sEMG signals compared to raw signals.

Conclusions:

  • The developed CEEMD-based denoising method effectively removes noise from sEMG signals while preserving essential information.
  • The proposed technique offers superior performance over existing methods, enhancing sEMG signal quality for various applications.
  • Improved sEMG signal quality leads to enhanced performance in applications such as hand motion recognition.