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

TL;DR

This paper introduces MSEMG, a lightweight neural network system combining the Mamba state space model with CNNs to effectively denoise surface electromyography signals contaminated by ECG interference, outperforming existing methods.

Contribution

The paper presents a novel MSEMG system that integrates Mamba and CNNs for efficient sEMG denoising, achieving superior performance with fewer parameters.

Findings

MSEMG outperforms existing denoising methods.

Generates higher-quality sEMG signals.

Uses fewer parameters for effective denoising.

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.