## 4 Performance Evaluation Results and Discussion
### Experiments and Results
#### 4.2.5 Result of Experiment 5  
As shown in Table 7, the optimized shallow domain knowledge that can be derived based on each heart disease diagnosis criteria exists. Non-medical experts, such as data scientists and developers, can learn this and inject it into the neural network through the proposed SDK-Injection method to improve the existing accuracy. However, there is no shallow domain knowledge that can be derived from medical facts for general ECG classification tasks that are not related to heart disease diagnosis, such as TwoLead. We hypothesized that, if there is no shallow domain knowledge that can be derived from medical facts, classification accuracy can be improved by setting the shallow domain \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Shallow Domain Knowledge**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**F1-Score**} \\ \cline{2-2} \cline{5-8}  & & **Start** & & & & & **End** \\  & & **Index** & & & & & & \\ \hline \multirow{3}{*}{AF-D1} & 0 & 0 & 0 & 20.00 & 0.17 & 0.20 & 0.60 & 0.18 \\  & Optimized & 70 & 300 & 46.67 & 0.47 & 0.47 & 0.73 & 0.46 \\  & Max Pattern & 0 & 640 & 40.00 & 0.45 & 0.40 & 0.70 & 0.29 \\ \hline \multirow{3}{*}{AF-D2} & 0 & 0 & 0 & 40.00 & 0.28 & 0.40 & 0.70 & 0.32 \\  & Optimized & 70 & 300 & 60.00 & 0.70 & 0.60 & 0.80 & 0.60 \\  & Max Pattern & 0 & 640 & 46.67 & 0.52 & 0.47 & 0.73 & 0.48 \\ \hline \multirow{3}{*}{ECG200} & 0 & 0 & 0 & 91.00 & 0.91 & 0.89 & 0.89 & 0.90 \\  & Optimized & 10 & 55 & 95.00 & 0.95 & 0.95 & 0.95 & 0.95 \\  & Max Pattern & 0 & 96 & 92.00 & 0.93 & 0.90 & 0.90 & 0.91 \\ \hline \multirow{3}{*}{ECG5000} & 0 & 0 & 0 & 94.02 & 0.68 & 0.55 & 0.98 & 0.58 \\  & Optimized & 100 & 135 & 94.38 & 0.75 & 0.58 & 0.98 & 0.63 \\  & Max Pattern & 0 & 140 & 94.11 & 0.66 & 0.55 & 0.98 & 0.58 \\ \hline \multirow{3}{*}{TwoLead} & 0 & 0 & 0 & 56.19 & 0.72 & 0.56 & 0.56 & 0.47 \\  & Optimized & 0 & 70 & 85.25 & 0.86 & 0.86 & 0.86 & 0.86 \\ \cline{1-1}  & Max Pattern & 0 & 82 & 85.25 & 0.86 & 0.85 & 0.85 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance evaluation of SDK-Injection among various lengths of shallow domain knowledge. knowledge to maximize the difference between class patterns. Based on this hypothesis, Table 9 shows the experimental results of one possible method for deriving a sub-pattern range that can replace shallow domain knowledge for general ECG classification tasks that are not related to heart disease. The accuracy of Table 9 is visualized in Figure 13. The red solid line in Figure 13 shows that the accuracy of the SDK-Injection method as the range of the sub-pattern to be set as shallow domain knowledge increased by 10 from index 0 to the pattern length 82. The black dotted line is the gradient of the absolute difference between the representative patterns of each class in the TwoLead dataset for the same situation. As a result, the change in accuracy according to the sub-pattern range almost coincided with the change of gradient of the absolute difference between the representative patterns of each class of TwoLead. That is, as the difference between the pattern values representing the two classes of TwoLead increased, the classification accuracy also increased. As a result, the  
Figure 12: Performance compared when applying optimized shallow domain knowledge and other length of shallow domain knowledge for each dataset: (**a**) accuracy when applying optimized shallow domain knowledge compared with length with 0 and maximum pattern length; (**b**) training time for the same situation.