Reconstruction of EMG Signals From Noisy Environment Using Sine Adapted Whale Optimization Algorithm
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Abstract
Electromyography (EMG) signals are frequently corrupted by noise during acquisition, impacting the accuracy of clinical diagnoses, particularly in neuromuscular disorder identification. Traditional adaptive noise cancellation filters, such as Least Mean Square (LMS) and Recursive Least Square (RLS) algorithms, face limitations in weight vector updating and parameter tuning, making them ineffective in eliminating common noise sources like electrocardiogram (ECG) noise, baseline wander, and power line interference. To overcome these limitations, we propose LMS-SiWOA and RLS-SiWOA, which integrate the Sine Adapted Whale Optimization Algorithm (SiWOA) to enhance signal-to-noise ratio (SNR) and optimize weight updates. The convergence analysis shows that the method successfully simplifies the optimization of adaptive filter coefficients by utilizing improved search capabilities of SiWOA. The proposed algorithms extract 17 key EMG features while effectively denoising the signal across multiple noise types. Experimental results show that the proposed LMS-SiWOA and RLS-SiWOA methods could improve SNR by 23.75% and 17% compared to conventional algorithms, providing a more reliable solution for clinical EMG signal processing.
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