SDEMG：用于表面肌电信号去噪的基于分数的扩散模型
SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising
AI总结：
本文提出SDEMG，一种基于分数的扩散模型，用于去除表面肌电信号中的心电干扰，实验表明其去噪性能优于对比方法并生成高质量sEMG样本。
摘要：
当被监测的肌肉靠近心脏时，表面肌电（sEMG）记录可能受到心电图（ECG）信号的干扰。现有若干方法采用基于信号处理的途径，如高通滤波和模板减法，而另一些方法则推导映射函数，以从含噪sEMG（带有ECG干扰的sEMG）中恢复干净的sEMG信号。近年来，基于分数的扩散模型作为一种著名的生成模型，已被引入用于在含噪输入数据下生成高质量且准确的样本。在本研究中，我们提出了一种新方法，称为SDEMG，即一种用于sEMG信号去噪的基于分数的扩散模型。为评估所提出的SDEMG方法，我们开展了降低sEMG信号噪声的实验，使用了来自公开可获取来源的数据，即Non-Invasive Adaptive Prosthetics数据库，以及来自MIT-BIH Normal Sinus Rhythm Database的ECG信号。实验结果表明，SDEMG优于对比方法，并生成了高质量的sEMG样本。SDEMG框架的源代码可在以下地址获取：https://github.com/tonyliu0910/SDEMG
英文摘要：
Surface electromyography (sEMG) recordings can be influenced by electrocardiogram (ECG) signals when the muscle being monitored is close to the heart. Several existing methods use signal-processing-based approaches, such as high-pass filter and template subtraction, while some derive mapping functions to restore clean sEMG signals from noisy sEMG (sEMG with ECG interference). Recently, the score-based diffusion model, a renowned generative model, has been introduced to generate high-quality and accurate samples with noisy input data. In this study, we proposed a novel approach, termed SDEMG, as a score-based diffusion model for sEMG signal denoising. To evaluate the proposed SDEMG approach, we conduct experiments to reduce noise in sEMG signals, employing data from an openly accessible source, the Non-Invasive Adaptive Prosthetics database, along with ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The experiment result indicates that SDEMG outperformed comparative methods and produced high-quality sEMG samples. The source code of SDEMG the framework is available at: https://github.com/tonyliu0910/SDEMG
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
