# Identification of Electrocardiographic Patterns Related to Mortality with COVID-19
## 4 Discussion  
This work aims to identify, with the application of the novel AdvRS&LP, electrocardiographic and vectorcardiographic patterns that may be related to mortality in subjects affected by COVID-19. The definition of \"death due to COVID-19\" is still under discussion. Indeed, most of the subjects whose mortality is associated with COVID-19 are also affected by other, pre-existing diseases; thus, the infection aggravates an already compromised condition. For this reason, we considered the ECG acquisition closest to the hospitalization date, considering the infection active during the recording, and we defined cases as subjects that died within one year from the infection, assuming the deaths were correlated with COVID-19. Due to the limited size of the database, the selected ECG signals were all employed for the 5-fold cross-validation procedure. Although this procedure is widely accepted in the literature [17, 18, 20, 21], the validation of the method in a testing holdout dataset would demonstrate the applicability of the method in a real-world application. Consequently, the classification results are purely indicative. Future studies will aim to demonstrate the reliability of the method in a real-world clinical scenario. Figure 3: Comparisons between AUC and FR for each NN and between the FRs of the different NNs. * refers to \(p\)-value lower than 0.05. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ranking**} & \multirow{2}{*}{**AUC (\%)**} & \multicolumn{5}{c}{**FR**} \\ \cline{3-8}  & & **Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** \\ \hline \multirow{2}{*}{8} & QA on lead V6 & QT on lead II & PT on lead V6 & QT on lead aVF & QT on lead II & PT on lead II \\  & (0.63) & (0.75) & (0.76 & (0.77) & (0.76) & (0.77) \\ \hline \multirow{2}{*}{9} & QRST-SA & QT on lead aVR & QT on lead V3 & PT on lead I & PT on lead I & QT on lead I \\  & (0.63) & (0.75) & (0.76 & (0.72) & (0.76) & (0.74) \\ \hline \multirow{2}{*}{10} & ToTpD & PT on lead V4 & QT on lead V2 & QT on lead V1 & PT on lead aVL & QT on lead aVL \\  & (0.63) & (0.75) & (0.75) & (0.75) & (0.73) & (0.74) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Despite these limitations, the NNs created through AdvRS&LP showed very promising classification capabilities, with a training AUC and ACC higher than 94% and 97%, respectively, and a validation AUC and ACC higher than 80% and 79%, respectively (Table 4). The AUC performed a little better than the ACC because the dataset was unbalanced (the number of controls was much larger than the number of cases). Considering the good performances, the application of AdvRS&LP for the identification of ECG/VCG patterns associated with mortality in COVID-19 patients seemed fitting. The architectural structuring of the artificial NNs was accomplished using the novel AdvRS&LP algorithm [25], an enhanced version of RS&LP recently proposed by [PERSON] et al. [23; 24]. Thanks to the removal of structural rules (previously included in RS&LP) and to a variable number of neurons to be added in each structuring step of the procedure, AdvRS&LP fosters the creation of NNs that adapt to the task for which they are needed, without compromising classification performance. Indeed, the architectures of the NNs obtained for the different folds are structurally different, since they are created with different data divisions, although they provide similar classification performances in both training (AUC = 97.38 \(\pm\) 2.25% and ACC = 98.42 \(\pm\) 1.17%) and hold-out testing (AUC = 84.31 \(\pm\) 2.58% and ACC = 80.40 \(\pm\) 1.77%). Classifiers created by AdvRS&LP can take into account a number of inputs that correspond to the number of features included in the training dataset. In this work, 254 features that covered the clinically relevant morphological, spatial, and temporal information for the ECG/VCG signals were extracted from 10-second 12-lead ECG signals.