Cleaning ECG with Deep Learning

Detalhes bibliográficos
Autor(a) principal: Dias, Mariana
Data de Publicação: 2024
Outros Autores: Probst, Phillip, Silva, Luís M., Gamboa, Hugo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/178492
Resumo: Funding Information: Open access funding provided by FCT|FCCN (b-on). This work was supported by Project OPERATOR (NORTE01-0247-FEDER-045910), cofinanced by the European Regional Development Fund through the North Portugal Regional Operational Program and Lisbon Regional Operational Program and by the Portuguese Foundation for Science and Technology, under the MIT Portugal Program. Publisher Copyright: © The Author(s) 2024.
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spelling Cleaning ECG with Deep LearningA Denoiser Tested in Industrial SettingsDenoiserECGGRUIndustryComputer Science(all)Computer Science ApplicationsComputer Networks and CommunicationsComputer Graphics and Computer-Aided DesignComputational Theory and MathematicsArtificial IntelligenceFunding Information: Open access funding provided by FCT|FCCN (b-on). This work was supported by Project OPERATOR (NORTE01-0247-FEDER-045910), cofinanced by the European Regional Development Fund through the North Portugal Regional Operational Program and Lisbon Regional Operational Program and by the Portuguese Foundation for Science and Technology, under the MIT Portugal Program. Publisher Copyright: © The Author(s) 2024.As the popularity of wearables continues to scale, a substantial portion of the population has now access to (self-)monitorization of cardiovascular activity. In particular, the use of ECG wearables is growing in the realm of occupational health assessment, but one common issue that is encountered is the presence of noise which hinders the reliability of the acquired data. In this work, we propose an ECG denoiser based on bidirectional Gated Recurrent Units (biGRU). This model was trained on noisy ECG samples that were created by adding noise from the MIT-BIH Noise Stress Test database to ECG samples from the PTB-XL database. The model was initially trained and tested on data corrupted with the three most common sources of noise: electrode motion artifacts, muscle activation and baseline wander. After training, the model was able to fully reconstruct previously unseen signals, achieving Root-Mean-Square Error values between 0.041 and 0.023. For further testing the model’s robustness, we performed a data collection in an industrial work setting and employed our model to clean the noisy data, acquired from 43 workers using wearable sensors. The trained network proved to be very effective in removing real ECG noise, outperforming the available open-source solutions, while having a much smaller complexity compared to state-of-the-art Deep Learning approaches.LIBPhys-UNLRUNDias, MarianaProbst, PhillipSilva, Luís M.Gamboa, Hugo2025-02-05T21:18:29Z2024-082024-08-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/178492eng2662-995XPURE: 106842050https://doi.org/10.1007/s42979-024-03017-7info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-03T01:37:57Zoai:run.unl.pt:10362/178492Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:46:51.191097Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Cleaning ECG with Deep Learning
A Denoiser Tested in Industrial Settings
title Cleaning ECG with Deep Learning
spellingShingle Cleaning ECG with Deep Learning
Dias, Mariana
Denoiser
ECG
GRU
Industry
Computer Science(all)
Computer Science Applications
Computer Networks and Communications
Computer Graphics and Computer-Aided Design
Computational Theory and Mathematics
Artificial Intelligence
title_short Cleaning ECG with Deep Learning
title_full Cleaning ECG with Deep Learning
title_fullStr Cleaning ECG with Deep Learning
title_full_unstemmed Cleaning ECG with Deep Learning
title_sort Cleaning ECG with Deep Learning
author Dias, Mariana
author_facet Dias, Mariana
Probst, Phillip
Silva, Luís M.
Gamboa, Hugo
author_role author
author2 Probst, Phillip
Silva, Luís M.
Gamboa, Hugo
author2_role author
author
author
dc.contributor.none.fl_str_mv LIBPhys-UNL
RUN
dc.contributor.author.fl_str_mv Dias, Mariana
Probst, Phillip
Silva, Luís M.
Gamboa, Hugo
dc.subject.por.fl_str_mv Denoiser
ECG
GRU
Industry
Computer Science(all)
Computer Science Applications
Computer Networks and Communications
Computer Graphics and Computer-Aided Design
Computational Theory and Mathematics
Artificial Intelligence
topic Denoiser
ECG
GRU
Industry
Computer Science(all)
Computer Science Applications
Computer Networks and Communications
Computer Graphics and Computer-Aided Design
Computational Theory and Mathematics
Artificial Intelligence
description Funding Information: Open access funding provided by FCT|FCCN (b-on). This work was supported by Project OPERATOR (NORTE01-0247-FEDER-045910), cofinanced by the European Regional Development Fund through the North Portugal Regional Operational Program and Lisbon Regional Operational Program and by the Portuguese Foundation for Science and Technology, under the MIT Portugal Program. Publisher Copyright: © The Author(s) 2024.
publishDate 2024
dc.date.none.fl_str_mv 2024-08
2024-08-01T00:00:00Z
2025-02-05T21:18:29Z
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/10362/178492
url http://hdl.handle.net/10362/178492
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2662-995X
PURE: 106842050
https://doi.org/10.1007/s42979-024-03017-7
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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