# An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention
## 5 Discussion  
With the gradual improvement of intelligent medical systems, improving the accuracy of automated analysis techniques of physiological signals and reducing the complexity of models have become the main directions of current research. In this paper, considering the sudden and fatal nature of cardiovascular diseases, we design an automated ECG signal quality assessment method to screen out valid ECG signals for subsequent early screening of abnormal ECG signals and to improve the efficiency of healthcare workers. The method is designed based on a modified Resnet and combined with a self-attentive module we designed in this paper. The design of this model not only avoids a lot of feature engineering design work caused by using traditional machine learning, but also avoids the problem of model performance degradation caused by a too-deep network model. Based on this, an attention mechanism is introduced to improve the accuracy of model prediction by paying more attention to the similarity information between individual heartbeats within the fragment when performing the prediction task. Moreover, the experimental results show that the attention module designed in this paper is more suitable for lightweight model design, which provides a new idea for the research of lightweight deep learning networks.  
Here, we compare the results of SA-Resnet18 with those of other works in recent years to evaluate the performance level of SA-Resnet18 in the area of ECG signal quality assessment. According to the data in Table 5, the SA-Resnet18 performs comparatively better when the combined accuracy and F1-score performance measures are taken into account. In terms of experimental methods, the SA-Resnet18 model is similar to [23] in that both do not require manual feature engineering; however, the SA-Resnet18 model has higher accuracy compared to [23], while the design of the SA-Resnet18 model verifies that the combination of these two network structures can show good performance in ECG signal quality assessment. In terms of the number of leads of experimental data, we use single-channel experimental data for model training and testing, which is more suitable for deployment on the mobile wearable health monitoring device side. This is because the acquisition and storage of single-lead data are more suitable for wearable health monitoring devices compared to 12-lead data.  
Figure 8: (**a**) An acceptable sample plot corresponds to (**a**) in Figure 7; (**b**) an unacceptable sample plot corresponds to (**b**) in Figure 7.  
However, a signal quality evaluation model has to be employed for numerous users of different genders and ages in the early morning, taking into account the variability of ECG signals among various persons. In the future, we plan to apply the concept of transfer learning to enhance the model's portability and robustness while maintaining patient privacy, which will further enhance the generalization capabilities of the model. Additionally, it is difficult to describe precisely which network phases or elements result in the final classification findings for the deep learning models we employ. Hence, we also need to work on the interpretability of the model.