Key result
OWAT outperforms EMD and conventional DWT for EMG denoising across key signal quality metrics.
Why the study?
EMG recordings are often corrupted by noise and artifacts, which significantly degrades signal interpretation, particularly in pathological cases like myopathy.
Does OWAT improve denoising of normal and myopathic EMG signals compared to EMD and conventional DWT?
Does OWAT improve denoising of normal and myopathic EMG signals compared to EMD and conventional DWT?
OWAT provides superior denoising of EMG signals compared to conventional methods, preserving essential physiological features even under low-SNR conditions.
Answers are grounded in this paper and cite their sources.
Enhances myopathic EMG interpretation in low-SNR settings; extends denoising performance beyond EMD and conventional DWT.
Electromyography (EMG) is widely used for diagnosing neuromuscular disorders by analyzing muscle electrical activity. However, EMG recordings are often corrupted by motion artifacts, power-line interference, and background noise, which significantly degrades signal interpretation, particularly in pathological cases such as myopathy. To address this challenge, this paper proposes an Optimal Wavelet Transform with Adaptive Thresholding (OWAT) for effective EMG signal denoising while preserving essential physiological features. The proposed approach automatically selects the most suitable wavelet basis from multiple wavelet families and determines the optimal threshold among four classical strategies— Sqtwolog, Rigrsure, Heursure, and Minimax—based on signal-to-noise ratio (SNR) improvement. The method is evaluated on both normal and myopathic EMG signals contaminated with additive Gaussian noise at various SNR levels. Experimental results show that the proposed OWAT method consistently outperforms EMD and conventional DWT in terms of MSE, RMSE, PRD, and SNR improvement, particularly under low-SNR conditions. These results confirm the robustness and effectiveness of the proposed approach for reliable EMG denoising in biomedical and clinical applications.
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Belkadi et al. (2026) studied Neuromuscular disorders (myopathy). Optimal Wavelet Transform with Adaptive Thresholding (OWAT) vs. Empirical Mode Decomposition (EMD) and conventional Discrete Wavelet Transform (DWT) was evaluated on MSE, RMSE, PRD, and SNR improvement. The proposed Optimal Wavelet Transform with Adaptive Thresholding (OWAT) method consistently outperformed EMD and conventional DWT in terms of MSE, RMSE, PRD, and SNR improvement for EMG denoising.
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