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On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals

2022-12-06Artigo científico Acesso aberto
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dc.contributor.authorEsgalhado, Filipa
dc.contributor.authorVassilenko, Valentina
dc.contributor.authorBatista, Arnaldo
dc.contributor.authorOrtigueira, Manuel
dc.contributor.institutionLIBPhys-UNL
dc.contributor.institutionUNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
dc.contributor.institutionCTS - Centro de Tecnologia e Sistemas
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2023-01-27T22:20:10Z
dc.date.available2023-01-27T22:20:10Z
dc.date.issued2022-12-06
dc.descriptionFunding Information: This work was funded by the Fundação para a Ciência e Tecnologia (FCT, Portugal) and NMT, S.A in the scope of the PhD grant PD/BDE/150312/2019 and by FCT within the scope of the CTS Research Unit—Center of Technology and Systems—UNINOVA, under the project UIDB/00066/2020 (FCT). Publisher Copyright: © 2022 by the authors.
dc.description.abstractHeart Rate Variability (HRV) is a biomarker that can be obtained non-invasively from the electrocardiogram (ECG) or the photoplethysmogram (PPG) fiducial points. However, the accuracy of HRV can be compromised by the presence of artifacts. In the herein presented work, a Simulink® model with a deep learning component was studied for overly noisy PPG signals. A subset with these noisy signals was selected for this study, with the purpose of testing a real-time machine learning based HRV estimation system in substandard artifact-ridden signals. Home-based and wearable HRV systems are prone to dealing with higher contaminated signals, given the less controlled environment where the acquisitions take place, namely daily activity movements. This was the motivation behind this work. The results for overly noisy signals show that the real-time PPG-based HRV estimation system produced RMSE and Pearson correlation coefficient mean and standard deviation of 0.178 ± 0.138 s and 0.401 ± 0.255, respectively. This RMSE value is roughly one order of magnitude above the closest comparative results for which the real-time system was also used.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent9
dc.format.extent4254484
dc.identifier.doi10.3390/computers11120177
dc.identifier.otherPURE: 51495392
dc.identifier.otherPURE UUID: 5d4bb932-7c9e-44c2-a7ce-80074a6ea383
dc.identifier.otherScopus: 85144645733
dc.identifier.otherORCID: /0000-0002-2287-4265/work/127269007
dc.identifier.otherORCID: /0000-0003-4270-3284/work/127269210
dc.identifier.otherWOS: 000902389800001
dc.identifier.urihttp://hdl.handle.net/10362/148292
dc.identifier.urlhttps://www.scopus.com/pages/publications/85144645733
dc.language.isoeng
dc.peerreviewedyes
dc.subjectdeep learning
dc.subjectheart rate variability
dc.subjectphotoplethysmogram
dc.subjectreal-time
dc.subjectSimulink
dc.subjectHuman-Computer Interaction
dc.subjectComputer Networks and Communications
dc.titleOn the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signalsen
dc.typejournal article
degois.publication.issue12
degois.publication.titleComputers
degois.publication.volume11
dspace.entity.typePublication
rcaap.rightsopenAccess

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