# _k_-Labelsets Method for Multi-Label ECG Signal Classification Based on SE-ResNet
## 3 Experimental Results  
### Multi-Label Classification Results Based on Superclass  
Table 3 shows the classification result of the ResNet, SE-ResNet, and the proposed model with \(k\)-labelsets. In the case of ResNet and SE-ResNet models, the respective results  
Figure 3: The overall framework for multi-label ECG classification based on SE-Resnet. using 18, 34, 50, and 101 layers are presented for comparison. In the case of plain ResNet, when 18 layers were used, exact match, F1-score, and recall showed the best at 60.67%, 92.06%, and 93.81%, respectively, and when 34 layers were used, accuracy showed the best at 88.02%. Additionally, the ResNet-50 model showed the highest precision at 91.06%. Meanwhile, in the case of SE-ResNet, when 34 layers were used, exact match, accuracy, and F1-score were the highest at 61.42%, 88.48%, and 92.40%. Furthermore, in order to compare the performance of the overall models, the performance of each model was presented as an average value. When comparing the averages, the results of using the squeeze-and-excitation network in all metrics were better than the results of plain ResNet. However, the increase in all metrics was less than 1% except exact match. In particular, SE-ResNet using 34 layers showed the best performance in all metrics except precision and recall. Therefore, in this study, the \(k\)-labelsets method was applied to the SE-ResNet-34 model, and the results were calculated by changing \(k=1,2,3,4\). When \(k=2\), F1-score and recall were the highest at 92.84% and 94.87%, and when \(k=4\), exact match, accuracy, and precision showed the best results at 63.76%, 89.16%, and 91.62%. In order to compare the results of all models, the highest value for each metric is shown in bold. Comparing the averages of plain ResNet, SE-ResNet, and the proposed models, the proposed model showed the best performance in all measures. Figure 4 shows the confusion matrices generated by the proposed model with 4-labelsets that showed the best accuracy performance. Figure 4a illustrates the confusion matrix on the classification of 'MI' ECG records. The figure demonstrates that the proposed model with 3-labelsets has correctly classified 3095 instances as absence of 'MI' and 1162 instances as presence of 'MI'. Similarly, Figure 4b shows the confusion matrix on the classification of 'STTC' ECG records. The figure exhibits that the proposed model has effectively classified 3041 instances as absence of 'STTC' and 802 instances as presence of 'STTC'. Likewise, Figure 4c displays the confusion matrix on the classification of 'CD' ECG records. The figure showcases that 3300 cases correctly classified the absence of 'CD' and 657 cases correctly classified the presence of 'CD'. Followed by, Figure 4d shows the confusion matrix on the classification of 'HYP' ECG records. The figure outperformed that the proposed method has effectively classified 3762 instances as absence of 'HYP' and 230 instances as presence of 'HYP'. In line with this, Figure 4e portrays the confusion matrix on the classification of 'NORM' ECG records. The figure demonstrates that 2113 cases correctly classified the absence of 'NORM' and 1722 cases correctly classified the presence of 'NORM'.