In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination is the presence of electrocardiogram (ECG) signals. To assess the quality of real-world sEMG data more effectively, this study proposes QASE-net, a new non-intrusive model that predicts the SNR of sEMG signals. QASE-net combines CNN-BLSTM with attention mechanisms and follows an end-to-end training strategy. Our experimental framework utilizes real-world sEMG and ECG data from two open-access databases, the Non-Invasive Adaptive Prosthetics Database and the MIT-BIH Normal Sinus Rhythm Database, respectively. The experimental results demonstrate the superiority of QASE-net over the previous assessment model, exhibiting significantly reduced prediction errors and notably higher linear correlations with the ground truth. These findings show the potential of QASE-net to substantially enhance the reliability and precision of sEMG quality assessment in practical applications.

翻译：在实际测量肌肉（特别是心脏附近区域）表面肌电信号（sEMG）时，主要污染源之一是心电（ECG）信号的干扰。为更有效地评估真实场景中sEMG数据的质量，本研究提出QASE-net，一种预测sEMG信号信噪比（SNR）的新型非侵入式模型。QASE-net融合了CNN-BLSTM与注意力机制，并采用端到端训练策略。实验框架利用来自两个开放数据库（非侵入式自适应假肢数据库和MIT-BIH正常窦性心律数据库）的真实sEMG与ECG数据。实验结果表明，QASE-net优于以往的评估模型，其预测误差显著降低，且与真实值的线性相关性明显提高。这些发现表明，QASE-net在提升实际应用中sEMG质量评估的可靠性与精确性方面具有巨大潜力。