## 4 Performance Evaluation Results and Discussion
### Experiments and Results
#### 4.2.2 Result of Experiment 2  
In general, when medical experts diagnose heart disease in hospitals, multi-ECG streams are measured through a professional device and these are integrated into the consideration. In other words, the ECG-based heart disease problem is a multivariate problem. Among the datasets used in the experiment, AF-D1 and AF-D2 were originally multivariate datasets for the diagnosis of atrial fibrillation. When the Base-CNN is used, multivariate analysis for atrial fibrillation diagnosis can be performed by configuring AF-D1 and AF-D2 as multivariate inputs as shown in Figure 7. On the other hand, the proposed method uses each of AF-D1 and AF-D2 as a univariate input in the form of a single ECG stream. That is, the existing multivariate problem can be solved by applying the SDK-Injection method with each of AF-D1 and AF-D2 as univariate inputs. Table 4 summarizes the accuracy, precision, sensitivity, specificity, and f1-score when solving the existing multivariate problem by applying the SDK-Injection method to the univariate input. Among them, the accuracy is visualized in Figure 8a. That is, Figure 8 shows the accuracy in (a) and the training time in (b) when solving the existing multivariate problem by applying the SDK-Injection method to the univariate input. When AF-D1 and AF-D2 are configured as multivariate inputs and analyzed using the Base-CNN, the accuracy is 20% higher than that of the Base-CNN-based analysis with AF-D1 as the univariate input. It is 6.67% higher than the accuracy when analyzing based on the Base-CNN with AF-D2 as a univariate input. This is because these AF-D1 and AF-D2 datasets for diagnosing atrial fibrillation are multivariate, and higher performance is shown when using multivariate input than when using univariate input based on a neural network with the same structure. Figure 7: Dual-variate ECG analysis with Base-CNN using dataset AF-D1 and AF-D2 for the diagnosis of atrial fibrillation. If the SDK-Injection method is applied with AF-D1 as a univariate input, it shows the same level of accuracy as that of the Base-CNN with multivariate input applied. In addition, the accuracy when applying the SDK-Injection method with AF-D2 as a univariate input is 13.33% higher than that of the Base-CNN with multivariate input applied. This shows that, by applying the SDK-Injection method, even though only the univariate input is used, equal or higher accuracy than the existing multivariate analysis can be achieved. However, the SDK-Injection method took the longest training time. Nevertheless, this experimental result shows the possibility of effectively solving the existing multi-ECG stream-based analysis problem by applying SDK-Injection to a single ECG stream, measured through a wearable device. In particular, the precision, sensitivity, specificity, and f1-scores, shown in Table 4, are also higher when the SDK-Injection is applied to \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{\begin{tabular}{c} **Base-CNN** \\ **(Univariate)** \\ \end{tabular} } & \
\begin{tabular}{c} **Base-CNN with** \\ **Multivariate** \\ **(AF-D1, AF-D2)** \\ \end{tabular} & \multirow{2}{*}{
\begin{tabular}{c} **SDK-Injection** \\ **(Univariate)** \\ \end{tabular} } \\ \hline Accuracy & AF-D1 & 26.67 & 46.67 & 46.67 \\ (\%) & AF-D2 & 40.00 & 60.00 \\ \hline Precision & AF-D1 & 0.30 & 0.48 & 0.47 \\  & AF-D2 & 0.33 & 0.48 & 0.70 \\ \hline Sensitivity & AF-D1 & 0.27 & 0.47 & 0.47 \\  & AF-D2 & 0.40 & 0.47 & 0.60 \\ \hline Specificity & AF-D1 & 0.63 & 0.73 & 0.73 \\  & AF-D2 & 0.70 & 0.73 & 0.80 \\ \hline F1-Score & AF-D1 & 0.28 & 0.45 & 0.46 \\  & AF-D2 & 0.35 & 0.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of univariate Base-CNN, multivariate Base-CNN, and SDK-Injection.