The application of deep learning algorithms for PPG signal processing and classification
Autor(a) principal: | |
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Data de Publicação: | 2021 |
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/10362/132796 |
Resumo: | Publisher Copyright: © 2021 by the authorsLicensee MDPI, Basel, Switzerland. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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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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1817545845623488512 |