## 4 Performance Evaluation Results and Discussion
### Experiments and Results  
In this paper, the performance of the proposed method is verified through a total of five experiments. Experiment 1 verifies the performance of the proposed method by comparing the accuracy of the existing Base-CNN with the case of applying the SDK-Injection method using the 5 datasets. Experiment 2 compares the performance when the SDK-Injection method is applied with AF-D1 and AF-D2 as univariate inputs, respectively, and when multivariate analysis is performed based on the Base-CNN, composed of multivariate inputs. In Experiment 3, the design of the SDK-Injection method, which combines the three techniques of Technique 1, Technique 2, and Technique 3, is experimentally verified. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{\begin{tabular}{c} **Size** \\ **(Train)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Pattern** \\ **(Test)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **No.** \\ **Length** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Class** \\ **of** \\ **Classes** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Class** \\ **Ratio** \\ **(Train)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Standard** \\ **Ratio** \\ **(Test)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Target** \\ **(Class Ratio)** \\ \end{tabular} } \\ \hline
1 & AF-D1 & 15 & 15 & 640 & 3 & 1 & 1 & 1 & 0 &
\begin{tabular}{c} Atrial \\ Fibrillation \\ \end{tabular} \\ \hline
2 & AF-D2 & 15 & 15 & 640 & 3 & 1 & 1 & 0 &
\begin{tabular}{c} Atrial \\ Fibrillation \\ \end{tabular} \\ \hline
3 & ECG200 & 100 & 100 & 96 & 2 & 1 & 1 & 1 & 0.71 & ischemia \\ \hline
4 & ECG5000 & 500 & 4500 & 140 & 5 & 5 & 3.91 & 58.82 &
\begin{tabular}{c} Congestive \\ Heart \\ Failure \\ \end{tabular} \\ \hline
5 & TwoLead & 23 & 1139 & 82 & 2 & 1.09 & 1 & 0.03 & Non-disease \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset Description. Experiment 4 verified how the optimized shallow domain knowledge derived from medical facts affects the accuracy improvement. Finally, Experiment 5 experimentally validates one kind of method that can derive shallow domain knowledge for general ECG classification tasks that are not for the purpose of diagnosing of heart disease. The five experiments evaluate performance of the SDK-Injection and all other compared methods, according to the following six metrics:  
* Accuracy \(=\) Test accuracy of the model (\(\%\));
* Precision \(=\frac{\textit{True Positive}}{\textit{True Positive}+\textit{False Positive}}\);
* Sensitivity \((\text{Recall})=\frac{\textit{True Positive}}{\textit{True Positive}+\textit{False Negative}}\);
* Specificity \(=\frac{\textit{True Negative}}{\textit{True Negative}+\textit{False Positive}}\);
* \(\text{F1}-\text{Score}=\frac{2\times\textit{Precision}\times\textit{ Sensitivity}}{\textit{Precision}+\textit{Sensitivity}}\);
* Training \(\text{Time}=\text{Training time of the model }(\sec)\). Accuracy is the test accuracy of the model evaluated based on the test dataset, and training time is the measured training time of the model in seconds. The precision is the ratio of the actual target labels among those classified as target labels by the model. Sensitivity or recall is the ratio of those classified as target labels by the model among those actual target labels. Additionally, since precision or sensitivity values may be biased in an imbalanced dataset, the performance can be evaluated using f1-score, which is the harmonic average of precision and sensitivity. Among the five datasets used in the experiment, there also exists multi-classification datasets with more than two classes. Therefore, the true positive, false positive, true negative, and false negative were calculated for each class in each dataset, and the precision, sensitivity, specificity, and f1-score for each class were calculated with this.