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A method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless device

2020Journal article Open access
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dc.contributor.authorMendonça, Fábio
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorDias, Fernando Morgado
dc.contributor.authorJulia-Serda, Gabriel
dc.contributor.authorRavelo-Garcia, Antonio G.
dc.date.accessioned2024-02-15T11:56:56Z
dc.date.available2024-02-15T11:56:56Z
dc.date.issued2020
dc.description.abstractThe 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMendonça, 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.pt_PT
dc.identifier.doi10.1109/ACCESS.2020.3019734pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5552
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationLaboratory for Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subject1DCNNpt_PT
dc.subjectCAPpt_PT
dc.subjectECGpt_PT
dc.subjectOSApt_PT
dc.subjectSleep qualitypt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleA method for sleep quality analysis based on CNN ensemble with implementation in a portable wireless devicept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory for Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F50009%2F2019/PT
oaire.citation.endPage158537pt_PT
oaire.citation.startPage158523pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.citation.volume8pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSilva Mendonça
person.familyNameMostafa
person.familyNameMorgado-Dias
person.givenNameFábio Rúben
person.givenNameSheikh Shanawaz
person.givenNameFernando
person.identifier34497
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0002-7677-0971
person.identifier.orcid0000-0001-7334-3993
person.identifier.ridN-9228-2015
person.identifier.scopus-author-id55489640900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationf90aafd0-eedb-47ea-945a-40b1c1fe802a
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relation.isAuthorOfPublication.latestForDiscovery042f7593-c6ca-4553-8f0e-12ccf17018be
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