###### Abstract  
ECG pattern classification for identifying the progress status of various heart diseases is a typical nonlinear problem. Therefore, deep learning-based automatic ECG diagnosis is being widely studied, and for this purpose, the CNN is mainly used to classify ECG patterns. In this case, it is hard to expect any further improvement in accuracy after optimizing the parameters. We propose a shallow domain knowledge injection method that can improve the accuracy of the existing parameter-optimized CNN. The proposed method can improve the accuracy by effectively injecting shallow domain knowledge, that can be acquired by non-medical experts, into the existing parameter-optimized CNN. The experiments show that the proposed method can be applied to both heart disease diagnoses and general ECG classification tasks, while improving the existing accuracy for both types of tasks. convolutional neural network; attention mechanism; ECG; time series data; classification 20221202212022120221202212022120221202212022120221202221202221202222120222222222222222222222222222222222222222222222222222222222  
methods diagnose each heart disease by applying a neural network to the results of ECG segmentation preprocessing [13; 14; 15]. Deep learning-based automatic ECG diagnosis techniques use CNN in various ways to classify ECG patterns. Deep learning is unexplainable [16; 17], and the most basic approach to reach target accuracy is parameter optimization. However, it is generally hard to expect any further improvement in accuracy after the parameters are optimized. Meanwhile, ECG-based heart disease diagnosis by medical experts with deep domain knowledge has been successfully performed from the past to the present and is the basis of modern clinical medicine. Therefore, it could be expected that the accuracy of existing parameter-optimized CNN for ECG classification could be improved by applying such deep domain knowledge. However, it is hard to apply deep domain knowledge directly into CNNs. First, using deep domain knowledge, in other words, applying the medical characteristics of ECG waveforms to classify ECG patterns, is a very complex nonlinear problem. Second, due to the fact that deep learning is unexplainable, it is hard to establish a theorical background that can reflect deep domain knowledge into the model design. Finally, since the subjects performing deep learning-based ECG analysis are non-medical experts, such as data analysts and developers, it is impossible to utilize deep domain knowledge without the help of medical experts--this eventually leads to a cost problem. In this paper, we propose a shallow domain knowledge injection (SDK-Injection) method that can additionally improve the accuracy of the existing parameter-optimized CNN. The proposed method applies curve fitting-based smoothing and an attention mechanism to inject shallow domain knowledge for a single ECG stream and expands the resulting variants to multivariate input. Through this, it is possible to effectively inject shallow domain knowledge that can be acquired by non-medical experts into the existing parameter-optimized CNN, and further improve the existing accuracy. The experiments show that the proposed method can be applied to both heart disease diagnosis and general ECG classification tasks and improve the accuracy for both types of tasks. The contributions of this study are as follows:  
* We propose a method to further improve the accuracy of the existing parameter-optimized CNN by using accumulated medical domain knowledge, which could not be utilized due to the inexplicability of deep learning. * By utilizing shallow domain knowledge that can be acquired even by non-medical experts, it is possible to further improve the existing accuracy while minimizing the cost of introducing medical experts. * By utilizing the existing CNN parameters that have already been optimized, the accuracy can be further improved without the cost of an additional parameter optimization process.