The application of deep learning algorithms for PPG signal processing and classification

Detalhes bibliográficos
Autor(a) principal: Esgalhado, Filipa
Data de Publicação: 2021
Outros Autores: Fernandes, Beatriz, Vassilenko, Valentina, Batista, Arnaldo, Russo, Sara
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/132796
Resumo: Publisher Copyright: © 2021 by the authorsLicensee MDPI, Basel, Switzerland.
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spelling The application of deep learning algorithms for PPG signal processing and classificationBiomedical signal processingCNNDeep learningLSTMNeural networksPPGRNNHuman-Computer InteractionComputer Networks and CommunicationsPublisher Copyright: © 2021 by the authorsLicensee MDPI, Basel, Switzerland.Photoplethysmography (PPG) is widely used in wearable devices due to its conveniency and cost-effective nature. From this signal, several biomarkers can be collected, such as heart and respiration rate. For the usual acquisition scenarios, PPG is an artefact-ridden signal, which mandates the need for the designated classification algorithms to be able to reduce the noise component effect on the classification. Within the selected classification algorithm, the hyperparameters’ adjustment is of utmost importance. This study aimed to develop a deep learning model for robust PPG wave detection, which includes finding each beat’s temporal limits, from which the peak can be determined. A study database consisting of 1100 records was created from experimental PPG measurements performed in 47 participants. Different deep learning models were implemented to classify the PPG: Long Short-Term Memory (LSTM), Bidirectional LSTM, and Convolutional Neural Network (CNN). The Bidirectional LSTM and the CNN-LSTM were investigated, using the PPG Synchrosqueezed Fourier Transform (SSFT) as the models’ input. Accuracy, precision, recall, and F1-score were evaluated for all models. The CNN-LSTM algorithm, with an SSFT input, was the best performing model with accuracy, precision, and recall of 0.894, 0.923, and 0.914, respectively. This model has shown to be competent in PPG detection and delineation tasks, under noise-corrupted signals, which justifies the use of this innovative approach.LIBPhys-UNLCTS - Centro de Tecnologia e SistemasUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasDF – Departamento de FísicaRUNEsgalhado, FilipaFernandes, BeatrizVassilenko, ValentinaBatista, ArnaldoRusso, Sara2022-02-12T23:27:18Z2021-122021-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/132796engPURE: 36755343https://doi.org/10.3390/computers10120158info: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:RCAAP2024-05-22T17:59:23Zoai:run.unl.pt:10362/132796Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:59:23Repositório 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 The application of deep learning algorithms for PPG signal processing and classification
title The application of deep learning algorithms for PPG signal processing and classification
spellingShingle The application of deep learning algorithms for PPG signal processing and classification
Esgalhado, Filipa
Biomedical signal processing
CNN
Deep learning
LSTM
Neural networks
PPG
RNN
Human-Computer Interaction
Computer Networks and Communications
title_short The application of deep learning algorithms for PPG signal processing and classification
title_full The application of deep learning algorithms for PPG signal processing and classification
title_fullStr The application of deep learning algorithms for PPG signal processing and classification
title_full_unstemmed The application of deep learning algorithms for PPG signal processing and classification
title_sort The application of deep learning algorithms for PPG signal processing and classification
author Esgalhado, Filipa
author_facet Esgalhado, Filipa
Fernandes, Beatriz
Vassilenko, Valentina
Batista, Arnaldo
Russo, Sara
author_role author
author2 Fernandes, Beatriz
Vassilenko, Valentina
Batista, Arnaldo
Russo, Sara
author2_role author
author
author
author
dc.contributor.none.fl_str_mv LIBPhys-UNL
CTS - Centro de Tecnologia e Sistemas
UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
DF – Departamento de Física
RUN
dc.contributor.author.fl_str_mv Esgalhado, Filipa
Fernandes, Beatriz
Vassilenko, Valentina
Batista, Arnaldo
Russo, Sara
dc.subject.por.fl_str_mv Biomedical signal processing
CNN
Deep learning
LSTM
Neural networks
PPG
RNN
Human-Computer Interaction
Computer Networks and Communications
topic Biomedical signal processing
CNN
Deep learning
LSTM
Neural networks
PPG
RNN
Human-Computer Interaction
Computer Networks and Communications
description Publisher Copyright: © 2021 by the authorsLicensee MDPI, Basel, Switzerland.
publishDate 2021
dc.date.none.fl_str_mv 2021-12
2021-12-01T00:00:00Z
2022-02-12T23:27:18Z
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/10362/132796
url http://hdl.handle.net/10362/132796
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 36755343
https://doi.org/10.3390/computers10120158
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 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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