MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network
- Authors: Liu Y.-T.; Wang K.-C.; Chao R.; Siniscalchi S.M.; Yeh P.-C.; Tsao Y.
- Publication year: 2025
- Type: Contributo in atti di convegno pubblicato in volume
- OA Link: http://hdl.handle.net/10447/679552
Abstract
Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal processing-based approaches, such as high-pass filtering and template subtraction, have been used to remove ECG interference but are often limited in their effectiveness. Recently, neural network-based methods have shown greater promise for sEMG denoising, but they still struggle to balance both efficiency and effectiveness. In this study, we introduce MSEMG, a novel system that integrates the Mamba state space model with a convolutional neural network to serve as a lightweight sEMG denoising model. We evaluated MSEMG using sEMG data from the Non-Invasive Adaptive Prosthetics database and ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The results show that MSEMG outperforms existing methods, generating higher-quality sEMG signals using fewer parameters.