Variational mode decomposition for surface and intramuscular EMG signal denoising. (April 2023)

• Record Type: Journal Article
• Authors: Ashraf, H.; Shafiq, U.; Sajjad, Q.; Waris, A.; Gilani, O.; Boutaayamou, M.; Brüls, O.
• Is Part Of: Biomedical signal processing and control. Volume 82(2023)
• Publication Date: 2023-04
• Subjects: EMG signal denoising -- Intramuscular EMG -- Variational mode decomposition; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104560
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Denoising of surface and intramuscular EMG signals using time–frequency decomposition. A novel Variational mode decomposition based denoising algorithm using SOFT iterative interval thresholding, effective equally for both surface and intramuscular EMG. A qualitative and quantitative analysis of the proposed method. To compare the proposed method with previously proposed methods based on Variational mode decomposition, Empirical mode decomposition and Wavelet denoising. To explore the advantages and disadvantages of the proposed denoising method. Abstract: Electromyographic signals contaminated with noise during the acquisition process affect the results of follow-up applications such as disease diagnosis, motion recognition, gesture recognition, and human–computer interaction. This paper proposes a denoising technique based on the variational mode decomposition (VMD) for both surface electromyography signals (sEMG) and intramuscular electromyography signals (iEMG). sEMG and iEMG obtained from 5 healthy subjects were first decomposed using VMD into respective variational mode functions (VMFs), then thresholds were set to remove the noise, and finally, the denoised signal was reconstructed. The denoising efficacy of interval thresholding (IT) and iterative interval thresholding (IIT) techniques in combination with SOFT, HARD, and smoothly clipped absolute deviation (SCAD) thresholding operators was quantitatively evaluated by using Signal to Noise Ratio (SNR) and further statistically validated by Friedman test. The results demonstrated that IIT provides better SNR values than IT at all noise levels (P-value < 0.05) for sEMG signals. For iEMG, IIT outperformed IT at 0db and 5db noise levels, but at a noise level of 10db and 15db, IT outperformed IIT. However, the results for the 10db noise level were statistically insignificant. The SOFT thresholding operator outperforms HARD and SCAD at all noise levels for sEMG, as well as iEMG (P-value < 0.05). The study demonstrates that the combination of the IIT thresholding technique with the VMD-based SOFT thresholding operator yields the best denoising results while retaining the original signal characteristics. The proposed method can be used in the fields of disease diagnosis, pattern recognition, and movement classification.