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

Autor:	Liu, Yu-Tung, Wang, Kuan-Chen, Chao, Rong, Siniscalchi, Sabato Marco, Yeh, Ping-Cheng, Tsao, Yu
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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 with fewer parameters. The source code for MSEMG is available at https://github.com/tonyliu0910/MSEMG. Comment: This paper is under review of 2025 ICASSP
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.18902 Zobrazit plný text záznamu View this record from Arxiv