MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network

• URL: http://arxiv.org/abs/2411.18902v2
• Date: Wed, 19 Feb 2025 04:53:42 GMT
• Title: MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network
• Authors: Yu-Tung Liu, Kuan-Chen Wang, Rong Chao, Sabato Marco Siniscalchi, Ping-Cheng Yeh, Yu Tsao,
• Abstract summary: Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. 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.
• Score: 21.596126531908908
• License:
• 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.

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