# Identification of Electrocardiographic Patterns Related to Mortality with COVID-19
## 1 Introduction  
COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, which has greatly affected healthcare systems on a worldwide level due to its high transmission rate and health consequences [1]. As of 25 October 2023, there had been more than 700 million cases since the beginning of the pandemic [1]. Among these cases, the virus has manifested in different manners, as infected people have experienced mild to severe symptoms related to the respiratory system [2, 3]. Severe manifestations of COVID-19 can require hospitalization in intensive care units [2, 3] and may also be lethal, as proven by the great number of deaths (773,819,856 from the beginning of the pandemic until the time of writing) [1], especially in elderly groups and/or in subjects with preexisting conditions like, for example, overweight. Even when not requiring hospitalization, COVID-19 infection often contributes to the worsening of an already unstable health status [1]. It has also been observed that COVID-19 may lead to manifestations in biological systems other than the respiratory system. Some of the most common manifestations of COVID-19 outside the respiratory system arise at the cardiac level, for example, arrhythmias, bundle branch blocks, acute coronary syndrome, myocarditis, and heart failure [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. As cardiac complications are prevalent among COVID-19 patients and have been observed in more than 50% of COVID-19 deaths [5; 6], timely identification by cardiac monitoring is essential. A common form of cardiac screening is the standard 10-second 12-lead electrocardiogram (ECG, Figure 1a), which, together with the vectorcardiogram (VCG, Figure 1b), can be used to identify the presence of cardiac abnormalities. Usually, ECG and VCG analyses are automatically performed, and cardiologists combine their interpretation with patients' anamnesis. However, interpretation is highly dependent on cardiologists' experience and can be affected by limitations in the data or intra-subject and intra-rater variability [14; 15; 16]. 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 largely investigated [17; 18; 19; 20; 21; 22]. The methods proposed in [17; 18; 19; 20; 21; 22] showed good accuracy, ranging from 85% to 100%, in detecting cardiovascular changes that may be related to COVID-19. However, complex deep learning models, such as convolutional neural networks (CNN), are not easily interpretable due to their nature (being black boxes). The non-explainability of the reasoning behind these complex models causes a lack of trust among clinicians and, thus, hampers their usage in clinical practice as diagnostic methods. [PERSON] et al. [23; 24] proposed a new machine learning method, called the Repeated Structuring & Learning Procedure (RS&LP), and tested it to detect heart failure, ischemia, and atrial fibrillation, with promising results. This method allows the construction of supervised neural networks (NNs) that were created by considering features as inputs and proved to be more interpretable than other deep learning models, which consider entire signals as inputs. The recent application of an advanced version of the RS&LP, namely Advanced Repeated Structuring & Learning Procedure (AdvRS&LP), for myocardial ischemia detection has seen further improvements in terms of NN performance [25]. Considering the good results obtained, the current work aims to investigate the use of the novel AdvRS&LP for the identification of electrocardiographic and vectorcardiographic patterns related to mortality with COVID-19. With this aim, the presented paper contributed to revealing the hidden electrocardiographic and vectorcardiographic patterns related to mortality with COVID-19, assessing the reliability of NNs created by AdvRS&LP in electrocardiographic feature interpretation and investigating interpretable solutions to support clinical practice using AI-based methods.