一种用于表面肌电信号的非侵入式神经质量评估模型
A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals
AI总结：
针对 ECG 污染下的 sEMG 质量评估问题，提出融合 CNN-BLSTM 与注意力机制的端到端 QASE-net，用于预测 SNR，并在两个开放数据库上验证其误差更低、相关性更高。
摘要：
在涉及测量肌肉表面肌电信号（sEMG）的实际场景中，尤其是在靠近心脏的区域，主要污染源之一是心电图（ECG）信号的存在。为了更有效地评估真实世界 sEMG 数据的质量，本研究提出了 QASE-net，这是一种预测 sEMG 信号信噪比（SNR）的新型非侵入式模型。QASE-net 将 CNN-BLSTM 与注意力机制相结合，并采用端到端训练策略。我们的实验框架分别使用了来自两个开放获取数据库的真实 sEMG 和 ECG 数据，即 Non-Invasive Adaptive Prosthetics Database 和 MIT-BIH Normal Sinus Rhythm Database。实验结果证明了 QASE-net 相较于先前评估模型的优越性，表现出显著降低的预测误差以及与真实值明显更高的线性相关性。这些发现表明，QASE-net 有潜力在实际应用中大幅提升 sEMG 质量评估的可靠性和精确性。
英文摘要：
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.
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