# _k_-Labelsets Method for Multi-Label ECG Signal Classification Based on SE-ResNet
## 4 Discussion  
This paper has proposed a model applying the \(k\)-labelsets methodology to SE-ResNet-34 to solve the multi-label ECG signal classification problem. Although the existing RAKEL algorithm randomly selected \(k\)-subsets, this study used all \(k\)-subsets and observed the performance of the model while changing the value of \(k\). In addition, the proposed model uses a soft voting method that sums the outputs of classifiers and passes through sigmoid activation, unlike the existing algorithm using majority voting. SE-ResNet-34 was used as a classifier to apply the \(k\)-labelsets methodology because this model showed the best performance while changing the depth of plain ResNet and SE-ResNet models. In both the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Exact Match** & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline  
*58.01** & **83.82** & 90.14 & **89.60** & 90.70 \\ \hline Average & 55.98 & 83.76 & 90.11 & 89.52 & 90.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification performance for the proposed model with \(k\)-labelsets based on subclass (The proposed model with \(k\)-labelsets uses SE-ResNet-34 as a classifier). plain ResNet model and the SE-ResNet model, even if the deep model was used, it did not show better performance. Although the residual block is structured so that the performance does not deteriorate even if the depth of the model is deep by using the shortcut connection, the deep model did not show good performance only for the classification problem of ECG data. CNN is suitable for image classification problems, and ECG data have a very small amount of data compared to images, so it does not seem to show good performance even if the depth of the model is deep. The results of comparing plain ResNet and SE-ResNet showed that the overall performance improved when the squeeze and excitation network was used, but the performance measures were improved by less than 1% except exact match. Among plain ResNet and SE-ResNet models the SE-ResNet model using 34 layers showed the best performance, and the \(k\)-labelsets method was applied to this model. In the multi-label classification problem based on superclass, the proposed model applying the \(k\)-labelsets methodology showed good overall performance regardless of the value of \(k\), and showed the best exact match, accuracies, and precision of 63.76%, 89.16%, and 91.62%, respectively, when \(k=4\). Furthermore, when \(k=2\), it showed the best F1-score and recall value of 92.84% and 94.87%. The performance of the proposed model in multi-label classification based on subclasses was also examined. The model with \(k=22\) showed the highest values in exact match, Accuracy, and precision, and was the model with the best overall performance. It was helpful to increase the value of \(k\) to take into account the values of other labels, as all labels must be matched correctly for exact match to be high. On the other hand, accuracy and F1-score were highest even when \(k=12\) and \(k=17\). Since these metrics do not need to consider the values of other labels, as the \(k\) increases, the values of the metrics increase and then decrease. However, since this number is not high enough to make a diagnosis, a follow-up study is needed to improve diagnosis accuracy by using not only ECG data but also demographic data, such as gender, height, and age. In addition, it is also necessary to analyze whether there are any differences between classifying 100 Hz ECG data and classifying 500 Hz ECG data. Conceptualization, M.-S.K. ; methodology, B.K. ; software, J.Y., Y.J. ; validation, J.Y., Y.J. ; formal analysis, J.Y., Y.J. ; investigation, [PERSON], [PERSON]; resources, B.K. ; data curation, J.Y. ; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, B.K., M.-S.K. All authors have read and agreed to the published version of the manuscript.