A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals
Part Of
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Start Page
1
End Page
5
ISBN
979-835037149-9
Date Issued
2024-07-15
Author(s)
DOI
10.1109/EMBC53108.2024.10782154
Abstract
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 integrates a one-dimensional Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BLSTM) layer and attention mechanisms, following 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 baseline method, 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.
Event(s)
46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024
SDGs
Publisher
IEEE
Type
conference paper