A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device
Autor(a) principal: | |
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Data de Publicação: | 2020 |
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/10400.13/5552 |
Resumo: | The quality of sleep can be affected by the occurrence of a sleep related disorder and, among these disorders, obstructive sleep apnea is commonly undiagnosed. Polysomnography is considered to be the gold standard for sleep analysis. However, it is an expensive and labor-intensive exam that is unavailable to a large group of the world population. To address these issues, the main goal of this work was to develop an automatic scoring algorithm to analyze the single-lead electrocardiogram signal, performing a minute-by-minute and an overall estimation of both quality of sleep and obstructive sleep apnea. The method employs a cross-spectral coherence technique which produces a spectrographic image that fed three one-dimensional convolutional neural networks for the classification ensemble. The predicted quality of sleep was based on the electroencephalogram cyclic alternating pattern rate, a sleep stability metric. Two methods were developed to indirectly evaluate this metric, creating two sleep quality predictions that were combined with the sleep apnea diagnosis to achieve the final global sleep quality estimation. It was verified that the quality of sleep of the nineteen tested subjects was correctly identified by the proposed model, advocating the significance of clinical analysis. The model was implemented in a non-invasive and simple to self-assemble device, producing a tool that can estimate the quality of sleep and diagnose the obstructive sleep apnea at the patient’s home without requiring the attendance of a specialized technician. Therefore, increasing the accessibility of the population to sleep analysis. |
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A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device1DCNNCAPECGOSASleep quality.Faculdade de Ciências Exatas e da EngenhariaThe quality of sleep can be affected by the occurrence of a sleep related disorder and, among these disorders, obstructive sleep apnea is commonly undiagnosed. Polysomnography is considered to be the gold standard for sleep analysis. However, it is an expensive and labor-intensive exam that is unavailable to a large group of the world population. To address these issues, the main goal of this work was to develop an automatic scoring algorithm to analyze the single-lead electrocardiogram signal, performing a minute-by-minute and an overall estimation of both quality of sleep and obstructive sleep apnea. The method employs a cross-spectral coherence technique which produces a spectrographic image that fed three one-dimensional convolutional neural networks for the classification ensemble. The predicted quality of sleep was based on the electroencephalogram cyclic alternating pattern rate, a sleep stability metric. Two methods were developed to indirectly evaluate this metric, creating two sleep quality predictions that were combined with the sleep apnea diagnosis to achieve the final global sleep quality estimation. It was verified that the quality of sleep of the nineteen tested subjects was correctly identified by the proposed model, advocating the significance of clinical analysis. The model was implemented in a non-invasive and simple to self-assemble device, producing a tool that can estimate the quality of sleep and diagnose the obstructive sleep apnea at the patient’s home without requiring the attendance of a specialized technician. Therefore, increasing the accessibility of the population to sleep analysis.IEEEDigitUMaMendonça, FábioMostafa, Sheikh ShanawazDias, Fernando MorgadoJulia-Serda, GabrielRavelo-Garcia, Antonio G.2024-02-15T11:56:56Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.13/5552engMendonça, F., Mostafa, S. S., Dias, M. F., Juliá-Serdá, G., & Ravelo-García, A. G. (2020). A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device. IEEE Access, 8, 158523-158537.10.1109/ACCESS.2020.3019734info: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:RCAAP2024-02-18T05:33:21Zoai:digituma.uma.pt:10400.13/5552Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:38:49.929563Repositório 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 method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device |
title |
A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device |
spellingShingle |
A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device Mendonça, Fábio 1DCNN CAP ECG OSA Sleep quality . Faculdade de Ciências Exatas e da Engenharia |
title_short |
A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device |
title_full |
A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device |
title_fullStr |
A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device |
title_full_unstemmed |
A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device |
title_sort |
A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device |
author |
Mendonça, Fábio |
author_facet |
Mendonça, Fábio Mostafa, Sheikh Shanawaz Dias, Fernando Morgado Julia-Serda, Gabriel Ravelo-Garcia, Antonio G. |
author_role |
author |
author2 |
Mostafa, Sheikh Shanawaz Dias, Fernando Morgado Julia-Serda, Gabriel Ravelo-Garcia, Antonio G. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
DigitUMa |
dc.contributor.author.fl_str_mv |
Mendonça, Fábio Mostafa, Sheikh Shanawaz Dias, Fernando Morgado Julia-Serda, Gabriel Ravelo-Garcia, Antonio G. |
dc.subject.por.fl_str_mv |
1DCNN CAP ECG OSA Sleep quality . Faculdade de Ciências Exatas e da Engenharia |
topic |
1DCNN CAP ECG OSA Sleep quality . Faculdade de Ciências Exatas e da Engenharia |
description |
The quality of sleep can be affected by the occurrence of a sleep related disorder and, among these disorders, obstructive sleep apnea is commonly undiagnosed. Polysomnography is considered to be the gold standard for sleep analysis. However, it is an expensive and labor-intensive exam that is unavailable to a large group of the world population. To address these issues, the main goal of this work was to develop an automatic scoring algorithm to analyze the single-lead electrocardiogram signal, performing a minute-by-minute and an overall estimation of both quality of sleep and obstructive sleep apnea. The method employs a cross-spectral coherence technique which produces a spectrographic image that fed three one-dimensional convolutional neural networks for the classification ensemble. The predicted quality of sleep was based on the electroencephalogram cyclic alternating pattern rate, a sleep stability metric. Two methods were developed to indirectly evaluate this metric, creating two sleep quality predictions that were combined with the sleep apnea diagnosis to achieve the final global sleep quality estimation. It was verified that the quality of sleep of the nineteen tested subjects was correctly identified by the proposed model, advocating the significance of clinical analysis. The model was implemented in a non-invasive and simple to self-assemble device, producing a tool that can estimate the quality of sleep and diagnose the obstructive sleep apnea at the patient’s home without requiring the attendance of a specialized technician. Therefore, increasing the accessibility of the population to sleep analysis. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 2020-01-01T00:00:00Z 2024-02-15T11:56:56Z |
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/10400.13/5552 |
url |
http://hdl.handle.net/10400.13/5552 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Mendonça, F., Mostafa, S. S., Dias, M. F., Juliá-Serdá, G., & Ravelo-García, A. G. (2020). A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device. IEEE Access, 8, 158523-158537. 10.1109/ACCESS.2020.3019734 |
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 |
IEEE |
publisher.none.fl_str_mv |
IEEE |
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 |
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