# _k_-Labelsets Method for Multi-Label ECG Signal Classification Based on SE-ResNet
## 2 Materials and Methods
### ResNet and Squeeze and Excitation Network  
#### 2.2.1 ResNet  
The purpose of neural networks classifier was to find a function that maps the input \(x\) to the label \(y\). This approach causes gradient vanishing problems when layers become deeper. In other words, as the layers get deeper, the gradient gradually decreases as the backpropagation process returns to the input layer. Therefore, the weights are not updated in the layer close to the input layer, making it difficult to find the mapping function that want to find. The advent of ResNet [30] made it possible to deal these issues. The gradient vanishing problem was solved by adding a shortcut that skips over the middle, rather than consecutively in the order of the connections between the layers. The shortcut connection calculates the residuals based on how much it has changed from the previous value, which  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Acronym**} & \multicolumn{2}{c}{**SCP Statement Description**} \\ \hline \multirow{4}{*}{Superclasses} & \multicolumn{2}{c}{NORM} & \multicolumn{2}{c}{Normal ECG} \\ \cline{2-4}  & \multicolumn{2}{c}{CD} & \multicolumn{2}{c}{Conduction Disturbance} \\ \cline{2-4}  & \multicolumn{2}{c}{MI} & \multicolumn{2}{c}{Myocardial Infarction} \\ \cline{2-4}  & \multicolumn{2}{c}{HYP} & \multicolumn{2}{c}{Hypertrophy} \\ \cline{2-4}  & \multicolumn{2}{c}{STTC} & \multicolumn{2}{c}{ST/T change} \\ \hline \multirow{4}{*}{CD} & NORM & NORM & Normal ECG \\ \cline{2-4}  & \multirow{4}{*}{CD} & LAFB/LPFB &
\begin{tabular}{c} left anterior/left posterior \\ fascicular block \\ \end{tabular} \\ \cline{2-4}  & \multirow{4}{*}{CD} & IRBBB & incomplete right bundle branch block \\ \cline{2-4}  & \multicolumn{2}{c}{ILBBB} & incomplete left bundle branch block \\ \cline{2-4}  & \multirow{2}{*}{CD} & CLBBB & complete left bundle branch block \\ \cline{2-4}  & \multirow{2}{*}{CRBBB} & complete right bundle branch block \\ \cline{2-4}  & \multirow{2}{*}{\(\_\)AVB} & AV block \\ \cline{2-4}  & \multirow{2}{*}{IVCB} & \multicolumn{2}{c}{non-specific intraventricular} \\ \cline{2-4}  & \multirow{2}{*}{\(\_\)} & conduction disturbance (block) \\ \cline{2-4}  & \multirow{2}{*}{\(\_\)} & WPW & \multicolumn{2}{c}{Wolf-Parkinson-White syndrome} \\ \hline \multirow{4}{*}{Subclasses} & LVH & left ventricular hypertrophy \\ \cline{2-4}  & \multirow{2}{*}{HYP} & RHV & right ventricular hypertrophy \\ \cline{2-4}  & \multirow{2}{*}{LAO/LAE} & left atrial overload/enlargement \\ \cline{2-4}  & \multirow{2}{*}{RAO/RAE} & \multirow{2}{*}{right atrial overload/enlargement} \\ \cline{2-4}  & \multirow{2}{*}{\(\_\)} & SEHYP & septal hypertrophy \\ \hline \multirow{4}{*}{MI} & AMI & anterior myocardial infarction \\ \cline{2-4}  & \multirow{2}{*}{IMI} & \multirow{2}{*}{inferior myocardial infarction} \\ \cline{2-4}  & \multirow{2}{*}{LMI} & \multirow{2}{*}{lateral myocardial infarction} \\ \cline{2-4}  & \multirow{2}{*}{PMI} & posterior myocardial infarction \\ \cline{2-4}  & \multirow{2}{*}{\(\_\)} & ISCA & ischemic in anterior leads \\ \cline{2-4}  & \multirow{2}{*}{STTC} & ISC\(\_\) & ischemic in inferior leads \\ \cline{2-4}  & \multirow{2}{*}{\(\_\)} & ISC\(\_\) & non-specific ischemic \\ \cline{2-4}  & \multirow{2}{*}{STTC} & STTC & ST-T changes \\ \cline{2-4}  & \multirow{2}{*}{\(\_\)} & NST\(\_\) & non-specific ST changes \\ \hline \hline \end{tabular}
\end{table}
Table 1: SCP-ECG acronym descriptions for super- and subclasses [19].  
ensures that the gradient delivered is at least 1. Figure 1 shows the structure of the residual block in ResNet architecture. It can be confirmed that \(\bar{X}\) is output by adding the input feature map \(X\) with \(C\times H\times W\) and the feature map that \(X\) passed through the residual block. When \(C\), \(H\), and \(W\) mean the number of channel, height, and width in feature map \(X\), respectively. Additionally, the details and variants of the network structure according to the number of layers used in this work can be found in Table 2.