A Systematic Review of Detecting Sleep Apnea Using Deep Learning
Autor(a) principal: | |
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Data de Publicação: | 2019 |
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/10400.13/5547 |
Resumo: | Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach. |
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A Systematic Review of Detecting Sleep Apnea Using Deep LearningCNNDeep learningSleep apneaSensors for sleep apneaRNNDeep neural network.Faculdade de Ciências Exatas e da EngenhariaEscola Superior de Tecnologias e GestãoSleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach.MDPIDigitUMaMostafa, Sheikh ShanawazMendonça, FábioRavelo-García, Antonio G.Dias, Fernando Morgado2024-02-09T16:15:31Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.13/5547engMostafa, S. S., Mendonça, F., G. Ravelo-García, A., & Morgado-Dias, F. (2019). A systematic review of detecting sleep apnea using deep learning. Sensors, 19(22), 4934.10.3390/s19224934info: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:RCAAP2024-02-11T04:56:43Zoai:digituma.uma.pt:10400.13/5547Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:37:47.110829Repositório 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 |
A Systematic Review of Detecting Sleep Apnea Using Deep Learning |
title |
A Systematic Review of Detecting Sleep Apnea Using Deep Learning |
spellingShingle |
A Systematic Review of Detecting Sleep Apnea Using Deep Learning Mostafa, Sheikh Shanawaz CNN Deep learning Sleep apnea Sensors for sleep apnea RNN Deep neural network . Faculdade de Ciências Exatas e da Engenharia Escola Superior de Tecnologias e Gestão |
title_short |
A Systematic Review of Detecting Sleep Apnea Using Deep Learning |
title_full |
A Systematic Review of Detecting Sleep Apnea Using Deep Learning |
title_fullStr |
A Systematic Review of Detecting Sleep Apnea Using Deep Learning |
title_full_unstemmed |
A Systematic Review of Detecting Sleep Apnea Using Deep Learning |
title_sort |
A Systematic Review of Detecting Sleep Apnea Using Deep Learning |
author |
Mostafa, Sheikh Shanawaz |
author_facet |
Mostafa, Sheikh Shanawaz Mendonça, Fábio Ravelo-García, Antonio G. Dias, Fernando Morgado |
author_role |
author |
author2 |
Mendonça, Fábio Ravelo-García, Antonio G. Dias, Fernando Morgado |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
DigitUMa |
dc.contributor.author.fl_str_mv |
Mostafa, Sheikh Shanawaz Mendonça, Fábio Ravelo-García, Antonio G. Dias, Fernando Morgado |
dc.subject.por.fl_str_mv |
CNN Deep learning Sleep apnea Sensors for sleep apnea RNN Deep neural network . Faculdade de Ciências Exatas e da Engenharia Escola Superior de Tecnologias e Gestão |
topic |
CNN Deep learning Sleep apnea Sensors for sleep apnea RNN Deep neural network . Faculdade de Ciências Exatas e da Engenharia Escola Superior de Tecnologias e Gestão |
description |
Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2019-01-01T00:00:00Z 2024-02-09T16:15:31Z |
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/10400.13/5547 |
url |
http://hdl.handle.net/10400.13/5547 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Mostafa, S. S., Mendonça, F., G. Ravelo-García, A., & Morgado-Dias, F. (2019). A systematic review of detecting sleep apnea using deep learning. Sensors, 19(22), 4934. 10.3390/s19224934 |
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.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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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 |
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1799137428504576000 |