Training strategies for Covid-19 severity classification

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

doi:10.1007/978-3-031-34953-9_40

Training strategies for Covid-19 severity classification

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

^*Corresponding author for this work

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

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Bioinformatics and Biomedical Engineering
Subtitle of host publication	10th International Work-Conference, IWBBIO 2023, Proceedings
Editors	Ignacio Rojas, Olga Valenzuela, Fernando Rojas Ruiz, Luis Javier Herrera, Francisco Ortuño
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	514-527
Number of pages	14
ISBN (Print)	9783031349522
DOIs	https://doi.org/10.1007/978-3-031-34953-9_40
Publication status	Published - Jun 2023
Event	10th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2023 - Meloneras, Spain Duration: 12 Jul 2023 → 14 Jul 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13919 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2023
Country/Territory	Spain
City	Meloneras
Period	12/07/23 → 14/07/23

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

COVID-19
Disease severity classification
Electrocardiogram (ECG)
Heart Rate Variability (HRV)
Signal processing

Access to Document

10.1007/978-3-031-34953-9_40

Cite this

Pordeus, D., Ribeiro, P., Zacarias, L., Oliveira, A. D., Marques, J. A. L., Rodrigues, P. M., Leite, C., Neto, M. A., Peixoto, A. A., & do Vale Madeiro, J. P. (2023). Training strategies for Covid-19 severity classification. In I. Rojas, O. Valenzuela, F. Rojas Ruiz, L. J. Herrera, & F. Ortuño (Eds.), Bioinformatics and Biomedical Engineering: 10th International Work-Conference, IWBBIO 2023, Proceedings (pp. 514-527). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13919 LNBI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-34953-9_40

Pordeus, Daniel ; Ribeiro, Pedro ; Zacarias, Laíla et al. / Training strategies for Covid-19 severity classification. Bioinformatics and Biomedical Engineering: 10th International Work-Conference, IWBBIO 2023, Proceedings. editor / Ignacio Rojas ; Olga Valenzuela ; Fernando Rojas Ruiz ; Luis Javier Herrera ; Francisco Ortuño. Springer Science and Business Media Deutschland GmbH, 2023. pp. 514-527 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{769db2a18e3a48d28def0567c58fdde0,

title = "Training strategies for Covid-19 severity classification",

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.",

keywords = "COVID-19, Disease severity classification, Electrocardiogram (ECG), Heart Rate Variability (HRV), Signal processing",

author = "Daniel Pordeus and Pedro Ribeiro and La{\'i}la Zacarias and Oliveira, \{Adriel de\} and Marques, \{Jo{\~a}o Alexandre Lobo\} and Rodrigues, \{Pedro Miguel\} and Camila Leite and Neto, \{Manoel Alves\} and Peixoto, \{Arnaldo Aires\} and \{do Vale Madeiro\}, \{Jo{\~a}o Paulo\}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 10th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2023 ; Conference date: 12-07-2023 Through 14-07-2023",

year = "2023",

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language = "English",

isbn = "9783031349522",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "514--527",

editor = "Ignacio Rojas and Olga Valenzuela and \{Rojas Ruiz\}, Fernando and Herrera, \{Luis Javier\} and Francisco Ortu{\~n}o",

booktitle = "Bioinformatics and Biomedical Engineering",

address = "Germany",

}

Pordeus, D, Ribeiro, P, Zacarias, L, Oliveira, AD, Marques, JAL, Rodrigues, PM, Leite, C, Neto, MA, Peixoto, AA & do Vale Madeiro, JP 2023, Training strategies for Covid-19 severity classification. in I Rojas, O Valenzuela, F Rojas Ruiz, LJ Herrera & F Ortuño (eds), Bioinformatics and Biomedical Engineering: 10th International Work-Conference, IWBBIO 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13919 LNBI, Springer Science and Business Media Deutschland GmbH, pp. 514-527, 10th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2023, Meloneras, Spain, 12/07/23. https://doi.org/10.1007/978-3-031-34953-9_40

Training strategies for Covid-19 severity classification. / Pordeus, Daniel; Ribeiro, Pedro; Zacarias, Laíla et al.
Bioinformatics and Biomedical Engineering: 10th International Work-Conference, IWBBIO 2023, Proceedings. ed. / Ignacio Rojas; Olga Valenzuela; Fernando Rojas Ruiz; Luis Javier Herrera; Francisco Ortuño. Springer Science and Business Media Deutschland GmbH, 2023. p. 514-527 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13919 LNBI).

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

TY - GEN

T1 - Training strategies for Covid-19 severity classification

AU - Pordeus, Daniel

AU - Ribeiro, Pedro

AU - Zacarias, Laíla

AU - Oliveira, Adriel de

AU - Marques, João Alexandre Lobo

AU - Rodrigues, Pedro Miguel

AU - Leite, Camila

AU - Neto, Manoel Alves

AU - Peixoto, Arnaldo Aires

AU - do Vale Madeiro, João Paulo

PY - 2023/6

Y1 - 2023/6

N2 - 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.

AB - 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.

KW - COVID-19

KW - Disease severity classification

KW - Electrocardiogram (ECG)

KW - Heart Rate Variability (HRV)

KW - Signal processing

UR - https://www.scopus.com/pages/publications/85164957594

U2 - 10.1007/978-3-031-34953-9_40

DO - 10.1007/978-3-031-34953-9_40

M3 - Conference contribution

SN - 9783031349522

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 514

EP - 527

BT - Bioinformatics and Biomedical Engineering

A2 - Rojas, Ignacio

A2 - Valenzuela, Olga

A2 - Rojas Ruiz, Fernando

A2 - Herrera, Luis Javier

A2 - Ortuño, Francisco

PB - Springer Science and Business Media Deutschland GmbH

T2 - 10th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2023

Y2 - 12 July 2023 through 14 July 2023

ER -

Pordeus D, Ribeiro P, Zacarias L, Oliveira AD, Marques JAL, Rodrigues PM et al. Training strategies for Covid-19 severity classification. In Rojas I, Valenzuela O, Rojas Ruiz F, Herrera LJ, Ortuño F, editors, Bioinformatics and Biomedical Engineering: 10th International Work-Conference, IWBBIO 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 514-527. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-34953-9_40

Training strategies for Covid-19 severity classification

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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