MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network
This work addresses denoising for sEMG signals in biomedical applications, representing an incremental improvement over prior neural network-based methods.
The paper tackled the problem of ECG interference in surface electromyography (sEMG) recordings by introducing MSEMG, a lightweight denoising model that integrates the Mamba state space model with a convolutional neural network, resulting in higher-quality sEMG signals using fewer parameters compared to existing methods.
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.