The Covid-19 pandemic, a disease transmitted by the SARS-CoV-2 virus, has already caused the infection of more than 120 million people, of which 70 million have been recovered, while 3 million people have died. The high speed of infection has led to the rapid depletion of public health resources in most countries. RT-PCR is Covid-19’s reference diagnostic method. In this work we propose a new technique for representing DNA sequences: they are divided into smaller sequences with overlap in a pseudo-convolutional approach and represented by co-occurrence matrices. This technique eliminates multiple sequence alignment. Through the proposed method, it is possible to identify virus sequences from a large database: 347,363 virus DNA sequences from 24 virus families and SARS-CoV-2. When comparing SARS-CoV-2 with virus families with similar symptoms, we obtained $$0.97 \pm 0.03$$ 0.97 ± 0.03 for sensitivity and $$0.9919 \pm 0.0005$$ 0.9919 ± 0.0005 for specificity with MLP classifier and 30% overlap. When SARS-CoV-2 is compared to other coronaviruses and healthy human DNA sequences, we obtained $$0.99 \pm 0.01$$ 0.99 ± 0.01 for sensitivity and $$0.9986 \pm 0.0002$$ 0.9986 ± 0.0002 for specificity with MLP and 50% overlap. Therefore, the molecular diagnosis of Covid-19 can be optimized by combining RT-PCR and our pseudo-convolutional method to identify DNA sequences for SARS-CoV-2 with greater specificity and sensitivity.

The proliferation of the SARS-CoV-2 virus to the whole world caused more than 250,000 deaths worldwide and over 4 million confirmed cases. The severity of Covid-19, the exponential rate at which the virus proliferates, and the rapid exhaustion of the public health resources are critical factors. The RT-PCR with virus DNA identification is still the benchmark Covid-19 diagnosis method. In this work we propose a new technique for representing DNA sequences: they are divided into smaller sequences with overlap in a pseudo-convolutional approach, and represented by co-occurrence matrices. This technique analyzes the DNA sequences obtained by the RT-PCR method, eliminating sequence alignment. Through the proposed method, it is possible to identify virus sequences from a large database: 347,363 virus DNA sequences from 24 virus families and SARS-CoV-2. Experiments with all 24 virus families and SARS-CoV-2 (multi-class scenario) resulted 0.822222 ± 0.05613 for sensitivity and 0.99974 ± 0.00001 for specificity using Random Forests with 100 trees and 30% overlap. When we compared SARS-CoV-2 with similar-symptoms virus families, we got 0.97059 ± 0.03387 for sensitivity, and 0.99187 ± 0.00046 for specificity with MLP classifier and 30% overlap. In the real test scenario, in which SARS-CoV-2 is compared to Coronaviridae and healthy human DNA sequences, we got 0.98824 ± 0.01198 for sensitivity and 0.99860 ± 0.00020 for specificity with MLP and 50% overlap. Therefore, the molecular diagnosis of Covid-19 can be optimized by combining RT-PCR and our pseudo-convolutional method to identify SARS-CoV-2 DNA sequences faster with higher specificity and sensitivity.

In December 2019, in the city of Wuhan, capital of the Province of Central China, a new specimen of coronavirus crossed the barriers between species and hit humans for the first time. A member of the Coronaviridae family and also associated with Severe Acute Respiratory Syndrome (SARS), similarly to its predecessor, SARS-CoV, the virus was named SARS-CoV-2 [46,51]. The new coronavirus is responsible for 2019 coronavirus disease, or Covid-19, a blood disorder that strongly affects the respiratory system, causing, in mild and moderate cases, fever, dry cough, decreased or loss of sense of smell and taste.In the most severe cases, the disease leads to decreased oxygen saturation in the blood and destruction of the surfactant inside the alveoli, which can lead to collapse, causing a respiratory deficiency that can worsen until death. SARS-CoV-2

In December 2019, the spread of the SARS-CoV-2 virus to the world gave rise to probably the biggest public health problem in the world: the COVID-19 pandemic. Initially seen only as a disease of the respiratory system, COVID-19 is actually a blood disease with effects on the respiratory tract. Considering its influence on hematological parameters, how does COVID-19 affect cardiac function? Is it possible to support the clinical diagnosis of COVID-19 from the automatic analysis of electrocardiography? In this work, we sought to investigate how COVID-19 affects cardiac function using a machine learning approach to analyze electrocardiography (ECG) signals. We used a public database of ECG signals expressed as photographs of printed signals, obtained in the context of emergency care. This database has signals associated with abnormal heartbeat, myocardial infarction, history of myocardial infarction, COVID-19, and healthy heartbeat. We propose a system to support the diagnosis of COVID-19 based on hybrid deep architectures composed of pre-trained convolutional neural networks for feature extraction and Random Forests for classification. We investigated the LeNet, ResNet, and VGG16 networks. The best results were obtained with the VGG16 and Random Forest network with 100 trees, with attribute selection using particle swarm optimization. The instance size has been reduced from 4096 to 773 attributes. In the validation step, we obtained an accuracy of 94%, kappa index of 0.91, and sensitivity, specificity, and area under the ROC curve of 100%. This work showed that the influence of COVID-19 on cardiac function is quite considerable: COVID-19 did not present confusion with any heart disease, nor with signs of healthy individuals. It is also possible to build a solution to support the clinical diagnosis of COVID-19 in the context of emergency care from a non-invasive and technologically scalable solution, based on hybrid deep learning architectures. Graphical Abstract

Purpose: From December 2019, the spread of the SARS-CoV-2 virus from the Chinese city of Wuhan to the world gave rise to probably the biggest public health problem in the world: the Covid-19 pandemic. Initially seen only as a disease of the respiratory system, the scientific community soon realized that it was a disease of the blood with effects on the respiratory tract. Several works presented solutions to support the diagnosis of Covid-19 from the analysis of hematological parameters. However, considering its influence on hematological parameters, how does Covid-19 affect cardiac function? Methods: In this work, we sought to investigate how Covid-19 affects cardiac function using a machine learning approach to analyze electrocardiography (ECG) signals. We used a public database of ECG signals expressed as photographs of printed signals, obtained in the context of emergency care. This database has signals associated with abnormal heartbeat, myocardial infarction, history of myocardial infarction, Covid-19, and healthy heartbeat. We propose a system to support the diagnosis of Covid-19 based on hybrid deep architectures composed of pre-trained convolutional neural networks for feature extraction and Random Forests for classification. We investigated the LeNet, ResNet and VGG16 networks. Results: The best results were obtained with the VGG16 and Random Forest network with 100 trees, with attribute selection using particle swarm optimization. The instance size has been reduced from 4096 to 773 attributes. In the validation step, we obtained an accuracy of 94%, kappa index of 0.91, and sensitivity, specificity and area under the ROC curve of 100%. Conclusion: This work showed that the influence of Covid-19 on cardiac function is quite considerable: Covid-19 did not present confusion with any heart disease, nor with signs of healthy individuals. It is also possible to build a solution to support the clinical diagnosis of Covid-19 in the context of emergency care from a non-invasive and technologically scalable solution, based on hybrid deep learning architectures.