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Erschienen in:
2023 | OriginalPaper | Buchkapitel
Training Strategies for Covid-19 Severity Classification
verfasst von : Daniel Pordeus, Pedro Ribeiro, Laíla Zacarias, Adriel de Oliveira, João Alexandre Lobo Marques, Pedro Miguel Rodrigues, Camila Leite, Manoel Alves Neto, Arnaldo Aires Peixoto Jr, João Paulo do Vale Madeiro
Erschienen in: Bioinformatics and Biomedical Engineering
Verlag: Springer Nature Switzerland
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Abstract
The COVID-19 pandemic has posed a significant public health challenge on a global scale. It is imperative that we continue to undertake research in order to identify early markers of disease progression, enhance patient care through prompt diagnosis, identification of high-risk patients, early prevention, and efficient allocation of medical resources. In this particular study, we obtained 100 5-min electrocardiograms (ECGs) from 50 COVID-19 volunteers in two different positions, namely upright and supine, who were categorized as either moderately or critically ill. We used classification algorithms to analyze heart rate variability (HRV) metrics derived from the ECGs of the volunteers with the goal of predicting the severity of illness. Our study choose a configuration pro SVC that achieved 76% of accuracy, and 0.84 on F1 Score in predicting the severity of Covid-19 based on HRV metrics.