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.

该研究提出了 QASE-net，这是一种新的非侵入式模型，用于预测 sEMG 信号的信噪比，实验结果显示 QASE-net 具有明显降低的预测误差和与真实结果更高的线性相关性，具备提高 sEMG 质量评估可靠性和精度的潜力。