Abstract:
Electromyography (EMG) serves as a vital diagnostic tool in medical and clinical research,
enabling the monitoring and analysis of muscle electrical activity. In medical diagnostics,
EMG aids in identifying and assessing neuromuscular syndromes, i.e. amyotrophic lateral
sclerosis (ALS). However, EMG signals are prone to various forms of noise and
interference, posing challenges to accurate data interpretation. Thus, the development of
robust denoising techniques is crucial for enhancing EMG signal quality and addressing
practical challenges in clinical diagnostics, rehabilitation, and neuromuscular research.
This research introduces an innovative methodology integrating Variational Mode
Decomposition (VMD) and Graph Signal Processing (GSP) to improve EMG signal
quality. Unlike conventional approaches like Continuous Wavelet Transform (CWT), this
study explores the untapped potential of VMD with Intrinsic Mode Functions (IMFs) 16
and GSP in EMG signal analysis. sEMG data collected from 10 subjects using the EMGUSB (OT Bioelettronica) underwent denoising techniques, specifically CWT, VMD, and
GSP. Evaluation of noise reduction performance reveals compelling results, with GSP
demonstrating superior noise reduction capabilities compared to VMD and CWT.
Specifically, GSP increases the SNR by 259.15 meanwhile decreases the RMSE by 0.07.
In comparison, VMD upturns SNR with 111.56 and declines RMSE of 0.15. While both
VMD and GSP outperform CWT, which exhibits SNR enhancements of 90.46 and RMSE
reductions by 0.15. Statistical analysis validates the significant improvements (p < 0.05)
provided by VMD and GSP over CWT across varying noise levels. Notably, VMD and
GSP collectively exhibit substantial enhancements in both SNR and RMSE metrics,
underscoring their efficacy in preserving signal fidelity while minimizing noise and
artifacts.
