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Classification of Severity of COVID-19 Patients Based on the Heart Rate Variability
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Authors/contributors
- Pordeus, Daniel (Author)
- Ribeiro, Pedro (Author)
- Zacarias, Laíla (Author)
- Paulo Madeiro, João (Author)
- Lobo Marques, Joao Alexandre (Author)
- Miguel Rodrigues, Pedro (Author)
- Leite, Camila (Author)
- Alves Neto, Manoel (Author)
- Aires Peixoto Jr, Arnaldo (Author)
- de Oliveira, Adriel (Author)
- Lobo Marques, Joao Alexandre (Editor)
- Fong, Simon James (Editor)
Title
Classification of Severity of COVID-19 Patients Based on the Heart Rate Variability
Abstract
The continuous development of robust machine learning algorithms in recent years has helped to improve the solutions of many studies in many fields of medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the severity of COVID-19 from electrocardiogram (ECG) signals. We retrospectively collected 100 5-minute ECGs from 50 patients in two different positions, upright and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.
Book Title
Computerized Systems for Diagnosis and Treatment of COVID-19
Place
Cham
Publisher
Springer International Publishing
Date
2023
Pages
155-177
Language
en
ISBN
978-3-031-30788-1
Accessed
10/10/23, 4:37 AM
Library Catalog
Springer Link
Extra
DOI: 10.1007/978-3-031-30788-1_10
Citation
Pordeus, D., Ribeiro, P., Zacarias, L., Paulo Madeiro, J., Lobo Marques, J. A., Miguel Rodrigues, P., Leite, C., Alves Neto, M., Aires Peixoto Jr, A., & de Oliveira, A. (2023). Classification of Severity of COVID-19 Patients Based on the Heart Rate Variability. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 155–177). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_10
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