## 3 SDK-Injection Method
### Technique 3: Expansion to Multivariate  
The SDK-Injection method constructs a multivariate input by adding three variants to the original data, which are single ECG pattern data, as shown in Figure 5, in order to effectively reflect the shallow domain knowledge to the classification task. Additionally, it is transferred to the existing parameter-optimized Base-CNN. The three additional variants are smoothed data, attention output and smoothed attention output. Smoothed data can  
Figure 4: T wave part as shallow domain knowledge (green) and SDK-Injecting attention. The sample patterns are for the diagnosis of ischemia. The fact is known that the shape of the T wave is one of the main criteria for the diagnosis of ischemia. Therefore, the range of the T wave part (green) is used as shallow domain knowledge and injected by SDK-Injecting attention. be created by applying curve fitting-based smoothing of Technique 1 to the original data. Attention output can be generated by applying SDK-Injecting attention of Technique 2 to the original data. Additionally, the smoothed attention output can be obtained by applying Technique 2 to the smoothed data, generated through Technique 1. Technique 3 composing the SDK-Injection method constructs a 2D-matrix multivariate form of input by adding, as variants, the smoothed data, attention output, and smoothed attention output, generated from the original data. A classification task with original data, which is a single ECG stream, as input is a univariate problem. However, the classification task with the data obtained through Technique 3 as new input is a multivariate problem. In other words, Technique 3 extends the existing single ECG-based analysis to a four-variate analysis. Therefore, features can be extracted by considering original data, smoothed data, attention output, and smoothed attention output, simultaneously. In addition, the existing parameters of the parameter-optimized Base-CNN can be used as they are without additional training. This is because the neural network structure is not changed while expanding to multivariate by constructing a multivariate input in the form of a 2D-matrix. The experiment in Section 4 shows that the classification accuracy is higher for the case when expansion through Technique 3 is performed following Technique 1 and Technique 2, than it is for the case when Technique 2 is applied alone. This means that the core technology  
Figure 5: Expansion from univariate to multivariate by adding variants. Added variants, such as smoothed data, attention output, and smoothed attention output, are generated from the original data. of the SDK-Injection method is Technique 3, which effectively applies the shallow domain knowledge injected through Technique 2 to the existing Base-CNN. Additionally, in the experiment in Section 4, due to Technique 3, the SDK-Injection method to a single ECG-based analysis achieves higher accuracy than when performing multiple ECG-based analysis. These experimental results show the possibility that the SDK-Injection method can be applied to a smart hospital or healthcare service in a wearable-based environment that measures a single ECG stream. ECG-based diagnosis in hospitals by medical experts is a typical multivariate problem. In order to diagnose a disease based on ECG, a hospital measures up to 12 ECG streams by attaching several electrodes to body positions determined according to medical standards. Since the shape of the ECG stream induced by each electrode is slightly different, it is said that the most accurate diagnosis is possible when these 12 lead ECG streams are considered simultaneously. In fact, some heart diseases can only be accurately diagnosed by measuring multiple ECG streams. Therefore, such multi-ECG stream-based analysis is not suitable for wearable-based smart hospital or healthcare services.