Repository logo
 
Publication

VO2 prediction based on physiologic and mechanical exercise measurements

2022-09Conference object Open access
Simple item page
dc.contributor.authorPereira, M. A. Pacheco
dc.contributor.authorAlmeida, R.
dc.contributor.authorDias, Hermínia Brites
dc.date.accessioned2023-08-28T11:02:20Z
dc.date.available2023-08-28T11:02:20Z
dc.date.issued2022-09
dc.description.abstractThe Cardiopulmonary Exercise Test (CPET) is a diagnostic test that evaluates the functional capacity of an individual through the integrated response of the cardiovascular, respiratory, and metabolic systems. VO2 max is the parameter that access functional capacity, although it’s difficult to achieve given the effort that implies. In recent years, an increase in computing capabilities combined with the available storage of large amounts of information has led to a heightened interest in machine learning (ML). We aimed in this study to enable CPET with ML models that allow predicting oxygen consumption in healthy individuals. The study methodology is based on the cleaning and exploratory analysis of a public database with about 992 CPETs performed on healthy individuals and athletes. To predict each value of VO2 (~569,000 instances), five ML algorithms were used (Random Forests, kNN, Neural Networks, Linear Regression, and SVM) with heart rate, respiratory rate, time from the beginning of the exam and treadmill speed, using a 20-fold cross-validation. The best result came from the Random Forest model, with a R2 of 0.88 and a RMSE of 334.34 ml.min-1. Furthermore, using the same methodology but different features, we tried to predict the VO2max with the 724 adult participants with a maximal test (RER≥1.05) but weaker results were obtained (the best model was the Linear Regression, with a R2 of 0.50 and a RMSE of 498.06 ml.min-1). Still, this model showed a better correlation with the real VO2max than the Wasserman equation (R=0.71 vs R=0.59). It is possible to predict with accuracy breath-by-breath VO2, based on easy-to-obtain physiological and mechanical measurements.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPereira MP, Almeida R, Dias H. VO2 prediction based on physiologic and mechanical exercise measurements. In: ERS International Congress 2022, Barcelona (Spain), September 4-6, 2022. Eur Respir J. 2022;60(Suppl):4270.pt_PT
dc.identifier.doi10.1183/13993003.congress-2022.4270pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/16418
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherEuropean Respiratory Societypt_PT
dc.relation.publisherversionhttps://www.ers-education.org/lr/show-details/?idP=270989pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectCardiopulmonary Exercise Testpt_PT
dc.subjectMachine learningpt_PT
dc.subjectPhysical activitypt_PT
dc.subjectPhysiological diagnostic servicespt_PT
dc.titleVO2 prediction based on physiologic and mechanical exercise measurementspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.issueSupplpt_PT
oaire.citation.startPage4270pt_PT
oaire.citation.volume60pt_PT
person.familyNameDias
person.givenNameMaria Hermínia Monteiro Brites
person.identifier.ciencia-idE211-FB1B-04E3
person.identifier.orcid0000-0003-1257-8734
person.identifier.ridAFL-8310-2022
person.identifier.scopus-author-id55671930200
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationb7b7e7d9-83b2-4d63-9176-4ed73cf6b939
relation.isAuthorOfPublication.latestForDiscoveryb7b7e7d9-83b2-4d63-9176-4ed73cf6b939

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
VO2 prediction based on physiologic and mechanical exercise measurements.pdf
Size:
310.89 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ESTeSL - Posters