  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.
