COVID-19’s influence on cardiac function: a machine learning perspective on ECG analysis

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. Consi...
Ausführliche Beschreibung

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 Ausführliche Beschreibung

Autor*in:	Gomes, Juliana Carneiro [verfasserIn] de Santana, Maíra Araújo Masood, Aras Ismael de Lima, Clarisse Lins dos Santos, Wellington Pinheiro

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	COVID-19 clinical diagnosis COVID-19 computer-aided diagnosis Electrocardiography Deep learning Hybrid deep architectures

Anmerkung:

© International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Medical & biological engineering & computing - Cham : Springer Nature, 1963, 61(2023), 5 vom: 20. Jan., Seite 1057-1081
Übergeordnetes Werk:	volume:61 ; year:2023 ; number:5 ; day:20 ; month:01 ; pages:1057-1081

Links:	Volltext

DOI / URN:	10.1007/s11517-023-02773-7

Katalog-ID:	SPR049990896