Variational mode decomposition for surface and intramuscular EMG signal denoising

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