## 4 Performance Evaluation Results and Discussion
### Experiments and Results
#### 4.2.3 Result of Experiment 3  
SDK-Injection method consists of three techniques: Technique 1, Technique 2, and Technique 3. By combining these three techniques, various methods can be derived, as shown in Table 5 in addition to SDK-Injection. Table 5 specifies the types of input data and combinations of techniques to construct Base-CNN, SDK-Injection method, and other possible comparison methods. Additionally, this can be schematically shown in Figures 9 and 10. First, the proposed SDK-Injection method injects shallow domain knowledge into the existing parameter-optimized Base-CNN by applying all three techniques. As comparison methods for this, Compared 1 performs only Technique 1, that is, curve fitting-based smoothing. Compared 2 injects shallow domain knowledge by applying only Technique 2, that is, SDK-Injecting attention. Compared 3 consists of Technique 1 and Technique 2, where the SDK-Injecting attention of Technique 2 is applied to the smoothed data by Technique 1. Compared 4 is composed of Technique 1 and Technique 3, where Technique 3 constructs multivariate input by adding variants obtained via curve fitting-based smoothing by Technique 1. Compared 4 has three options according to the number of variants added. Compared 5 is composed of Technique 2 and Technique 3, where the SDK-Injecting attention injects shallow domain knowledge into the data which is added as variants to construct a dual-variate input. Table 6 summarizes the accuracy, precision, sensitivity, specificity, and f1-score of each dataset when applying the proposed SDK-Injection, Base-CNN and other compared methods given in Table 5 and Figure 10. Among them, the accuracy is visualized in Figure 11a. That is, Figure 11 compares the performance of the proposed SDK-Injection, Base-CNN, and other comparison methods for each dataset. First, for all datasets, SDK-Injection shows the highest accuracy compared against Base-CNN or other comparison methods. Additionally, in Table 6, the precision, sensitivity, specificity, and f1-score of the SDK-Injection are the highest. However, as an exception, the specificity of ECG5000 has the same value of 0.98 in the SDK-Injection and all compared methods. In other words, it was empirically proved that it was appropriate to design the SDK-Injection method by combining all three techniques. In addition, we can infer which technique affects the improvement in accuracy by comparing the accuracy of other comparison methods. \begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Name** & **Technique 1** & **Technique 2** & **Technique 3** & **Variants** \\ \hline
1 & Base-CNN & X & X & X & 1: Original Data \\ \hline
2 & SDK-Injection & O & O & O & 1: Original Data \\  & & & & & 2: Smoothed Data \\  & & & & & (21 _window size, 13 order_) \\  & & & & & 3: Attention Output \\  & & & & & 4: Smoothed Attention Output \\ \hline
3 & Compared 1 & O & X & X & 1: Original Data \\ \hline
4 & Compared 2 & X & O & X & 1: Original Data \\ \hline
5 & Compared 3 & O & O & X & 1: Original Data \\ \hline \end{tabular}
\end{table} 
Table 5: Description of SDK-Injection and other comparison methods derived from the combination of three techniques. In all datasets except AF-D2, the accuracy was improved when applying Compared 2, which consists of Technique 2 alone. Additionally, Compared 5, which consists of Technique 2 and Technique 3, also helped improve accuracy for all datasets except AF-D1. Compared 3, which consists of Technique 1 and Technique 2, helped to improve the accuracy for AF-D1, ECG5000, and TwoLead, and maintained the same accuracy in ECG200, but decreased the accuracy for AF-D2. The sensitivity and specificity of Compared 2 and Compared 5 also mostly increased than those of the Base-CNN. In summary, injecting shallow domain knowledge through SDK-Injecting attention of Technique 2 can help improve performance.