SSEMG-Net：一种基于频谱图的Mamba网络用于表面肌电信号去噪
SSEMG-Net: A Spectrogram-Based Mamba Network for Surface Electromyography Denoising
另 1 家 · 查看机构详情
- National Taiwan University（国立台湾大学）
- Academia Sinica（中央研究院）
- Stevens Institute of Technology（史蒂文斯理工学院）
- Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
提出SSEMG-Net，一种基于频谱图的Mamba网络，利用双向Mamba和生理感知的低频掩蔽去噪sEMG中的ECG伪影，实现更优信号质量与更低特征提取误差。
中文摘要 AI 辅助
当肌肉在心脏附近被记录时，表面肌电信号（sEMG）中经常出现心电图（ECG）伪影污染。现有的基于神经网络（NN）的方法通常采用逐点损失进行波形级端到端去噪，但往往无法保留sEMG的频谱结构。此外，这些模型没有利用ECG主要扭曲sEMG低频段这一先验知识。我们提出了SSEMG-Net，一种频谱图域模型，它使用双向Mamba主干和用于幅度掩蔽和包裹相位估计的双头解码器显式捕获sEMG-ECG交互。幅度掩蔽模块具有生理感知性，将去噪限制在ECG活动占主导地位的低频子带内。实验结果表明，与先前方法相比，SSEMG-Net实现了更优的信号质量和更低的特征提取误差，突显了其在保留sEMG频谱特征至关重要的临床应用中的潜力。
英文摘要
Electrocardiogram (ECG) artifact contamination frequently occurs in surface electromyography (sEMG) when muscles are recorded near the heart. Existing neural network (NN)-based approaches typically perform waveform-level end-to-end denoising with pointwise losses, but often fail to preserve the spectral structures of sEMG. In addition, these models do not exploit the prior knowledge that ECG mainly distorts the low-frequency band of sEMG. We propose SSEMG-Net, a spectrogram-domain model that explicitly captures sEMG-ECG interactions using a bidirectional Mamba backbone and a dual-head decoder for magnitude masking and wrapped-phase estimation. The magnitude masking module is physiologically aware, restricting denoising to the low-frequency sub-band where ECG activity predominates. Experimental results demonstrate that SSEMG-Net achieves superior signal quality and lower feature-extraction error compared to prior methods, underscoring its potential for clinical applications where preserving sEMG spectral features is essential.
