Classification of severity of COVID-19 patients based on the heart rate variability

Daniel Pordeus; Pedro Ribeiro; Laíla Zacarias; João Paulo Madeiro; Joao Alexandre Lobo Marques; Pedro Miguel Rodrigues; Camila Leite; Manoel Alves Neto; Arnaldo Aires Peixoto Jr; Adriel de Oliveira

doi:10.1007/978-3-031-30788-1_10

Classification of severity of COVID-19 patients based on the heart rate variability

Daniel Pordeus, Pedro Ribeiro, Laíla Zacarias, João Paulo Madeiro, Joao Alexandre Lobo Marques, Pedro Miguel Rodrigues, Camila Leite, Manoel Alves Neto, Arnaldo Aires Peixoto Jr, Adriel de Oliveira

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Computerized systems for diagnosis and treatment of COVID-19
Editors	Marques João Alexandre Lobo, Simon James Fong
Publisher	Springer
Chapter	10
Pages	155-177
Number of pages	23
ISBN (Print)	9783031307874, 9783031307881
DOIs	https://doi.org/10.1007/978-3-031-30788-1_10
Publication status	Published - 26 Jun 2023

Keywords

Electrocardiogram (ECG) signal
Heart Rate Variability (HRV) indices
Signal processing
Severity
COVID-19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-30788-1_10

Cite this

Pordeus, D., Ribeiro, P., Zacarias, L., Madeiro, J. P., Marques, J. A. L., Rodrigues, P. M., Leite, C., Neto, M. A., Jr, A. A. P., & Oliveira, A. D. (2023). Classification of severity of COVID-19 patients based on the heart rate variability. In M. João Alexandre Lobo, & S. J. Fong (Eds.), Computerized systems for diagnosis and treatment of COVID-19 (pp. 155-177). Springer. https://doi.org/10.1007/978-3-031-30788-1_10

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keywords = "Electrocardiogram (ECG) signal, Heart Rate Variability (HRV) indices, Signal processing, Severity, COVID-19",

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Pordeus, D, Ribeiro, P, Zacarias, L, Madeiro, JP, Marques, JAL, Rodrigues, PM, Leite, C, Neto, MA, Jr, AAP & Oliveira, AD 2023, Classification of severity of COVID-19 patients based on the heart rate variability. in M João Alexandre Lobo & SJ Fong (eds), Computerized systems for diagnosis and treatment of COVID-19. Springer, pp. 155-177. https://doi.org/10.1007/978-3-031-30788-1_10

Classification of severity of COVID-19 patients based on the heart rate variability. / Pordeus, Daniel; Ribeiro, Pedro; Zacarias, Laíla et al.
Computerized systems for diagnosis and treatment of COVID-19. ed. / Marques João Alexandre Lobo; Simon James Fong. Springer, 2023. p. 155-177.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Ribeiro, Pedro

AU - Zacarias, Laíla

AU - Madeiro, João Paulo

AU - Marques, Joao Alexandre Lobo

AU - Rodrigues, Pedro Miguel

AU - Leite, Camila

AU - Neto, Manoel Alves

AU - Jr, Arnaldo Aires Peixoto

AU - Oliveira, Adriel de

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Classification of severity of COVID-19 patients based on the heart rate variability

Abstract

Keywords

UN SDGs

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