# An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention
## 6 Conclusions  
Deep learning has successfully automated the classification and diagnosis of multi-channel ECG signals. However, there are still some problematic issues, such as high annotation costs, high memory requirements, and computational resource requirements during the training process. Additionally, it is very challenging to detect multi-channel signals, especially 12-channel signals, using mobile-embedded health detection devices. On the contrary, single-channel signals and single-channel ECG signals are relatively easy to detect. However, in remote locations, particularly in remote mountainous regions where medical knowledge is scarce, it is desirable to record valid ECG signals from patients which are helpful for subsequent ECG diagnosis and increase the effectiveness of medical specialists; theoretically, health detection devices can reduce abnormal alarms and automatically screen valuable ECG signals for the subsequent diagnosis of heart health conditions. To address the aforementioned requirements, we propose a self-attention module. Furthermore, the Resnet18 structure is optimized and enhanced to adapt to one-dimensional ECG signal analysis. The similarity principle of heartbeats within ECG recording segments is used as auxiliary judgment information to obtain correlation information and improve the performance of the signal quality assessment of the model. The experiment results also demonstrate that SA-Resnet performs well when evaluating the quality of 1D ECG data in single leads. In addition to making up for the lack of research on residual networks in the area of ECG signal analysis, it also develops a lightweight attention model that enhances network signal quality evaluation performance without adding a lot of computation or parametric variables.  
Conceptualization, Y.L. and H.Z.; methodology, Y.L. and K.Z.; software, Y.L.; validation, Y.L. and Y.Y.; formal analysis, Y.L.; investigation, Y.L. and F.L.; resources, H.Z. and K.Z.; data curation, Y.L. and L.C.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L., H.Z. and H.L.; visualization, Y.L.; supervision, H.Z.; project administration, Y.L.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (2020 YFC2003304).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Methods** & **Datasets** & **Lead Number** & **Accuracy** & **F1-Score** \\ \hline [PERSON] et al., 2019 [50] & Machine learning & Cinc2011 & 2 & 85.7\% & Unknown \\ [PERSON] et al., 2020 [23] & GoogLeNet Combining the & & & \\  & deep-learned Stockwell & & & \\  & Transform (S-Transform) & & & \\  & spectrogram features and & & & \\  & hand-crafted statistical & & & \\  & features & & & \\  & Decision tree & & & \\  & Covariance characteristic & & & \\  & matrix and machine & & & \\  & learning classifier & & & \\  & Resnet18+Self-Attention & & & & \\ \hline \hline \end{tabular}
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Table 5: Performance metrics between the model proposed in this study and various common networks are compared.