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
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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 - http://www.scopus.com/inward/record.url?scp=85164957594&partnerID=8YFLogxK
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 -