用轻量Mamba模型提升肌电图去噪效果
MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network
- 结合Mamba与卷积网络，构建高效去噪架构
- 参数更少却显著提升肌电信号质量
- 适合嵌入式设备实时处理肌电数据
表面肌电（sEMG）信号常因靠近心脏而受心电（ECG）干扰。传统基于信号处理的方法如高通滤波和模板减除效果有限。近年神经网络方法虽有进展，但仍难兼顾效率与性能。本文提出MSEMG，将Mamba状态空间模型与卷积神经网络结合，构建轻量级sEMG去噪模型。在Non-Invasive Adaptive Prosthetics数据库的sEMG数据和MIT-BIH Normal Sinus Rhythm Database的心电数据上评估，结果表明MSEMG在使用更少参数的情况下，生成的肌电信号质量优于现有方法。
原文摘要 · Abstract (English)
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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