## 5 Conclusions  
In this paper, we proposed the SDK-Injection method, that can further improve the existing ECG pattern classification accuracy by injecting shallow domain knowledge, which can be acquired by non-medical experts, into the existing parameter-optimized CNN. The proposed method was designed by combining three techniques, which are curve fitting-based smoothing, shallow domain knowledge injection based on the attention mechanism, and using variants obtained from these for expansion to multivariate input. Additionally, it showed the highest performance among the compared methods composed of different possible combinations of techniques. In addition, the experiments showed that the proposed method can be applied to both heart disease diagnosis and general ECG classification tasks, improving the original accuracy for both types of tasks. Interestingly,  
Figure 13: Accuracy (red) and gradient of differences between values of representative patterns of each class (black) according to the length of the subpattern starting from the index 0 for the TwoLead dataset, which is a general ECG classification task not related to heart disease. by applying the proposed method to single ECG-based analysis, the accuracy could be improved compared with that of multi-ECG-based analysis. For heart disease diagnosis tasks, the highest performance improvement was achieved when appropriate shallow domain knowledge, corresponding to each heart disease, was injected. On the other hand, there is no corresponding shallow domain knowledge for general ECG pattern classification tasks that are not related to the diagnosis of heart disease. In this case, the highest performance improvement was achieved when the range where the change in the difference value was accumulated the most was injected, instead of the shallow domain knowledge. The proposed method showed a high improvement in performance for balanced data, but a relatively low improvement in performance for imbalanced data. Therefore, we plan to study a method for significantly improving performance even when the proposed method is applied to imbalanced data. Second, it should be possible to inject more diverse kinds of shallow domain knowledge, for example, the order in which each sub-pattern appears, and not only the current range of sub-patterns which are the criteria for diagnosis of each heart disease. Thirdly, we plan to expand our approach which calculates the gradient after finding the difference between the representative pattern values for each class, and work toward applying the proposed method to general ECG classification tasks, beyond than the diagnosis of heart disease. Additionally, we plan to collect additional datasets other than the current AF-D1 and AF-D2, and develop a method of converting a multi-ECG-based analysis task to a single ECG-based analysis task, by applying the proposed method. Finally, we plan to further research on applying our method to various types of neural networks, for example, using generative neural networks to generate variants for the Technique 3. In particular, the proposed method can currently only be applied on the CNN-based model. However, LSTM is also being used in various ways for ECG analysis, either alone or in combination with CNN. Therefore, we will develop an advanced SDK-Injecting attention, which can apply the proposed method to LSTM-based neural networks. Through these future works, the proposed method can be developed as a core technology for various ECG-based diagnoses in smart hospitals or healthcare services, in an actual wearables-based environment. Methodology, software, and writing, S.O. ; writing--review and editing, M.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1062559). Not applicable. No new data were created or analyzed in this study.