On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/148292 |
Resumo: | Funding 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. |
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On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signalsdeep learningheart rate variabilityphotoplethysmogramreal-timeSimulinkHuman-Computer InteractionComputer Networks and CommunicationsFunding 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.Heart 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.LIBPhys-UNLUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasCTS - Centro de Tecnologia e SistemasRUNEsgalhado, FilipaVassilenko, ValentinaBatista, ArnaldoOrtigueira, Manuel2023-01-27T22:20:10Z2022-12-062022-12-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article9application/pdfhttp://hdl.handle.net/10362/148292engPURE: 51495392https://doi.org/10.3390/computers11120177info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T18:08:37Zoai:run.unl.pt:10362/148292Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T18:08:37Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals |
title |
On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals |
spellingShingle |
On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals Esgalhado, Filipa deep learning heart rate variability photoplethysmogram real-time Simulink Human-Computer Interaction Computer Networks and Communications |
title_short |
On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals |
title_full |
On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals |
title_fullStr |
On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals |
title_full_unstemmed |
On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals |
title_sort |
On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals |
author |
Esgalhado, Filipa |
author_facet |
Esgalhado, Filipa Vassilenko, Valentina Batista, Arnaldo Ortigueira, Manuel |
author_role |
author |
author2 |
Vassilenko, Valentina Batista, Arnaldo Ortigueira, Manuel |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
LIBPhys-UNL UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias CTS - Centro de Tecnologia e Sistemas RUN |
dc.contributor.author.fl_str_mv |
Esgalhado, Filipa Vassilenko, Valentina Batista, Arnaldo Ortigueira, Manuel |
dc.subject.por.fl_str_mv |
deep learning heart rate variability photoplethysmogram real-time Simulink Human-Computer Interaction Computer Networks and Communications |
topic |
deep learning heart rate variability photoplethysmogram real-time Simulink Human-Computer Interaction Computer Networks and Communications |
description |
Funding 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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-06 2022-12-06T00:00:00Z 2023-01-27T22:20:10Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/148292 |
url |
http://hdl.handle.net/10362/148292 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
PURE: 51495392 https://doi.org/10.3390/computers11120177 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
9 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
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1817545912418828288 |