## 3 SDK-Injection Method  
The proposed SDK-Injection method is designed, as shown in Figure 1, by combining the following three techniques, and through this, the accuracy of the original parameter-optimized CNN for ECG pattern classification, that is, the Base-CNN, can be further improved.  
* Technique 1: Curve fitting-based smoothing;
* Technique 2: SDK-Injecting attention;
* Technique 3: Expansion to multivariate.  
The proposed method applies curve fitting-based smoothing and an attention mechanism to inject shallow domain knowledge on the single ECG stream pattern which is the original data input. Additionally, the resulting variants are expanded to multivariate input delivered to the Base-CNN. Through this, it is possible to effectively inject into Base-CNNs the shallow domain knowledge at a level that non-medical experts can acquire, and the accuracy of the original Base-CNN can be further improved. The experimental results in Section 4 show that the SDK-Injection method, designed by combining all three techniques, shows the best performance when compared with the methods designed with other possible combinations of Technique 1, Technique 2, and Technique 3.  
Technique 1 applies curve fitting-based smoothing to the original data. Through this, the adverse effect of the noise included in the original data could be offset during the feature extraction process. Technique 2 inserts shallow domain knowledge through an attention mechanism. The resulting data from applying Technique 2 has a relatively high weight for the sub-pattern range, in other words, the shallow domain knowledge, which is known as important criteria for diagnosis of target heart disease. Technique 3 composes a  
Figure 1: SDK-Injection method proposed in this paper. It takes univariate original data, which is a single ECG stream pattern, and applies three techniques. Technique 1 is curve fitting-based smoothing by Savitzky–Golay filter and Technique 2 is SDK-Injecting attention to inject shallow domain knowledge. Technique 3 is expansion to multivariate, which expands the univariate input to multivariate input by adding three variants generated from the original data by applying Technique 1 and Technique 2. Multivariate input, generated by applying the SDK-Injection method, is passed to the existing parameter-optimized Base-CNN.  
four-variate input by adding three variants to the original data. Variants to be added are smoothed data, attention output, and smoothed attention output, as shown in Figure 1. Smoothed data is data from which noise has been removed by applying Technique 1 to the original data. Attention output is data with high weight for shallow domain knowledge by applying Technique 2 to the original data. Smoothed attention output is data obtained by applying Technique 2 to the smoothed data, obtained by applying Technique 1 to the original data. That is, the smoothed attention output is data with high weights for shallow domain knowledge in the noise-removed ECG pattern. Technique 3 composes the original data, smoothed data, attention output, and smoothed attention output in the form of a two-dimensional array and delivers it as input to the existing parameter-optimized Base-CNN.  
In particular, Technique 3 which is the expansion to multivariate technique that composes a four-variate input by adding three variants to the original single ECG stream pattern, contributes greatly to the improvement of accuracy through SDK-Injection. This is shown by the experimental results in Section 4, where the performance of the SDK-Injection method designed including Technique 3 is remarkably better than the methods designed only with the combination of Technique 1 and Technique 2. The three techniques constructing the SDK-Injection method are described in more detail below.