## 4 Performance Evaluation Results and Discussion
### Experiments and Results
#### 4.2.1 Result of Experiment 1  
Table 2 summarizes the accuracy, precision, sensitivity, specificity, and f1-score when the proposed SDK-Injection method is applied to each dataset and when only the Base-CNN is applied. The last column of the table summarizes how much the performance has improved from the Base-CNN by applying the SDK-Injection method. Among the metrics in the table, the accuracy is visualized in Figure 6a. That is, Figure 6a is the result of comparing the accuracy when the proposed SDK-Injection method is applied to each dataset and the accuracy when the existing Base-CNN is applied. When the SDK-Injection method was applied to all five datasets, the accuracy was improved compared with the case when only the Base-CNN was applied. However, the improvement in accuracy differed depending on the dataset. Figure 6b shows the difference between the accuracy when the SDK-Injection method was applied to each dataset and when only the Base-CNN was applied, along with the class imbalance of each dataset. AF-D1, AF-D2, and TwoLead are balanced datasets. On the other hand, ECG200 and ECG5000 are imbalanced datasets. At this time, the accuracy improvement when SDK-Injection is applied is very large, from about 15.36% to 20% in balanced datasets. However, for imbalanced datasets, the accuracy improvement when SDK-Injection is applied is relatively low. In particular, AF-D1 and AF-D2 showed the most uniform class distribution with a Standard Deviation (Class Ratio) value of 0, and the accuracy improvement was the largest at 20% when the proposed technique was applied. TwoLead has a nearly uniform class distribution, but is relatively imbalanced dataset compared with AF-D1 and AF-D2. Additionally, it can be seen that the accuracy improvement when the proposed method is applied is 15.36%, which is slightly lower than AF-D1 and AF-D2. ECG5000, the most imbalanced dataset, shows the lowest accuracy improvement at 0.62%. That is, the accuracy improvement when the  
SDK-Injection method is applied is inversely proportional to the degree of class imbalance of the dataset. As shown in Table 2, not only the accuracy but also the precision, sensitivity, specificity, and f1-score improved when SDK-Injection was applied to all datasets. Similar to the accuracy, the improvement is remarkable in the balanced dataset, and relatively small performance improvement is shown in the imbalanced dataset. Therefore, the proposed SDK-Injection method shows higher performance on a balanced dataset. Table 3 summarizes the precision, sensitivity, specificity, and f1-score for each class in each dataset for the same experiment situation. Class s of AF-D1, class s of AF-D2, and class 3 of ECG5000 showed sensitivity values of 0.20, 0.00, and 0.07, respectively, and precision values of 0.14, 0.00, and 0.43 in the Base-CNN, respectively. That is, these classes have relatively low classification performance compared with other classes in the same dataset. Interestingly, by applying SDK-Injection, the classification performance of classes which previously had very low classification performance was greatly improved. The sensitivity of these three classes were improved to 0.40, 0.40, and 0.41, respectively, after applying the SDK-Injection. The precision was also improved to 0.50, 1.00, and 0.60, respectively. In particular, in the case of class 3 of ECG5000, an imbalanced dataset, the f1-score also improved from 0.12 to 0.49. In conclusion, by applying the SDK-Injection, the performance was improved for not only the accuracy but also the precision, sensitivity, specificity, and f1-score for all datasets. In addition, it can be seen that the SDK-Injection can significantly improve classification performance for those classes that previously showed low classification performance, as well as classes that have been classified well in the past.