## 4 Performance Evaluation Results and Discussion
### Experiments and Results
#### 4.2.3 Result of Experiment 3
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Name** & **Technique 1** & **Technique 2** & **Technique 3** & **Variants** \\ \hline
6 & \
\begin{tabular}{c} Compared 4 \\ _(add 1 variant)_ \\ \end{tabular} & O & X & O &
\begin{tabular}{c} 1: Original Data \\ 2: Smoothed Data \\ (21 window size, 15 order)_ \\ \end{tabular} \\ \hline
7 & \
\begin{tabular}{c} Compared 4 \\ _(add 2 variants)_ \\ \end{tabular} & O & X & O &
\begin{tabular}{c} 1: Original Data \\ 2: Smoothed Data \\ (21 window size, 15 order) \\ \end{tabular} \\ \hline
8 & \
\begin{tabular}{c} Compared 4 \\ _(add 3 variants)_ \\ \end{tabular} & O & X & O &
\begin{tabular}{c} 1: Original Data \\ 2: Smoothed Data \\ (21 window size, 15 order) \\ \end{tabular} \\ \hline
9 & \
\begin{tabular}{c} Compared 5 \\ \end{tabular} & X & O & O &
\begin{tabular}{c} 1: Original Data \\ 2: Attention Output \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._  
Figure 9: Schemes of SDK-Injection and other comparison method (Base-CNN) derived from the combination of three techniques. On the other hand, Compared 1, consisting of only curve fitting-based smoothing of Technique 1, helped to improve the accuracy for AF-D1, AF-D2, and ECG5000, but decreased the accuracy in TwoLead, and maintained the same accuracy for ECG200. Additionally, Compared 4, which adds variants generated through Technique 1, did not help to improve accuracy for the rest of the datasets except for ECG200 and ECG5000. Even for TwoLead, when Compared 4 and Compared 1 were applied, the accuracy was rather decreased. When we consider the comparison methods based on Technique 2, those without Technique 1, which are Compared 2 or Compared 5, did not reduce the accuracy, but those including Technique 1--Compared 3--decreased the accuracy, with respect to AF-D2. The sensitivity and specificity of Compared 4 in most cases were also the same or decreased when compared with the Base-CNN. That is, the curve fitting-based smoothing  
Figure 10: Schemes of other comparison methods (from Compared 1 to Compared 5) derived from the combination of three techniques. of Technique 1 helps to improve accuracy as a preprocessing method, but it may distort important features depending on the dataset. Interestingly, the accuracy improvement of the SDK-Injection method is the most significant than that of any other comparison methods. In particular, as shown by the results of Compared 2, Compared 3, and Compared 5, the injection of shallow domain knowledge based on Technique 2 helps to improve accuracy; moreover, the SDK-Injection method is significantly high in accuracy compared with the accuracy of Compared 2, Compared 3, and Compared 5. The precision, sensitivity, specificity, and f1-scores, shown in Table 6, are also remarkably high in the case of SDK-Injection. This means that, although the shallow domain knowledge is injected by SDK-Injecting attention of Technique 2, the accuracy improvement can be further increased if the injected shallow domain knowledge is transmitted to the neural network by constructing multivariate input according to Technique 3 of the SDK-Injection method. As a result, the effect of the injected shallow domain knowledge on accuracy improvement can be maximized according to the method of transferring the injected shallow domain knowledge to the neural network. Additionally, it can be said that the SDK-Injection method is designed to maximize the effect of the injected shallow domain knowledge on accuracy improvement, compared with other comparison methods.