# Identification of Electrocardiographic Patterns Related to Mortality with COVID-19
## 4 Discussion
The use of features, as in this work, rather than raw or processed signal sequences guarantees a certain level of interpretability in NN classification. We decided not to normalize the input features: this design choice will guarantee the interpretability of the features by clinical personnel, which allows comparisons with normality ranges. Moreover, we decided not to apply the preprocessing feature-selection method. During the training of the NN, the adjustment of weights and biases automatically selects the feature of interest, embedding the feature-selection procedure inside the NN construction. The complexity of deep learning models can hinder the uncovering of the decision process behind classification, which is, however, fundamental in a diagnostic context. Notably, no anamnestic or clinical features were considered as inputs for the NNs, with the aim of underlining hidden cardiac patterns associated with mortality with COVID-19. To permit the interpretability of the NN created by AdvRS&LP, LIME was applied [35; 36; 37]. The use of LIME allowed the construction of a feature ranking, which highlighted the ECG/VCG features that most significantly affected the classification performed by the NNs and, therefore, the ECG/VCG features whose changes could be mainly associated with mortality in cases involving COVID-19. According to this analysis, HR and electrocardiographic intervals seem to be of relevance for classification, as features involving the mnNN, PT interval, and QT interval appeared in the top 10 on the FR ranking of all the NNs (Table 5), suggesting that changes in the duration of cardiac electrical activity might be related to mortality in COVID-19 cases. This finding is in agreement with the body of clinical research, which already revealed the modification of the QT interval [38], ST-T pattern [39; 40; 41], and PR interval [42] induced by COVID-19 infection. Notably, modifications to the QT interval are, indeed, often associated with a higher risk of developing arrhythmias or heart failure [2; 43], both of which have been observed in COVID-19 patients [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. The agreement between the FRs extracted from the different NNs (\(\rho\) > 0.97, Figure 2) suggests robust feature interpretation by LIME. The rankings obtained from LIME and the conventional statistical analysis, with the latter usually evaluated by clinicians, showed disagreement (\(\rho\) < 0.30). This disagreement was surely due to the different approaches: while conventional ROC analysis is based on a linear separation boundary between samples by considering the role of each single variable regardless of possible interactions, NNs build highly nonlinear separation boundaries by considering all variables simultaneously during model training. The conventional statistical analysis gave relevance to the features related to repolarization and RR-interval variability,as evidenced by the top 10 on the univariate AUC ranking (Table 5). From a physiological perspective, a relationship between the RR interval and the QT interval cannot be denied, as changes in one lead to variations in the other [44]. Thus, future studies will be needed to investigate the correlation between QT and RR features exclusively. In the context of the detection and investigation of cardiac risk associated with COVID-19, the use of machine learning and deep learning methods has already been investigated [17; 18; 19; 20; 21; 22] (Table 6). All these studies are based on electrocardiography, but they differ in terms of the type of input: three papers [17; 18; 19] considered the ECG paper scan of the open access \"ECG image dataset of Cardiac and COVID-19 patients\" database [45], two studies [20; 22] considered ECG signals collected on a private database, and one paper [21] considered ECG signals on the COVID-SQUARE database. Most of the papers [17; 18; 19; 21] investigated the detection of COVID-19 among those with healthy status or other diseases, one study [23] assessed the severity of the COVID-19 infection, and [PERSON] et al.