Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/54945
Campo DC Valoridioma
dc.contributor.authorMendonca, Fabioen_US
dc.contributor.authorMostafa, Sheikh Shanawazen_US
dc.contributor.authorMorgado-Dias, Fernandoen_US
dc.contributor.authorRavelo-Garcia, Antonio G.en_US
dc.date.accessioned2019-02-18T15:50:03Z-
dc.date.available2019-02-18T15:50:03Z-
dc.date.issued2018en_US
dc.identifier.issn1534-4320en_US
dc.identifier.urihttp://hdl.handle.net/10553/54945-
dc.description.abstractThe gold standard for assessment of sleep quality is the polysomnography, where physiological signals are used to generate both quantitative and qualitative measurements. Despite the production of highly accurate results, polysomnographyis a complex, uncomfortable, and expensive process, inaccessible to a large group of the population. Home monitoring devices were developed to address these issues, fitting the growing perspective of health care and focusing on prevention and wellness. The objective of this paper was to develop an algorithm capable of estimating the quality of sleep, by analyzing the cyclic alternating pattern rate. The algorithm uses a single-lead electrocardiogram to produce a spectrographic measure of the cardiopulmonary coupling that in turn was fed to a classifier to estimate the non-rapid eye movement sleep and the presence of the cyclic alternating pattern. Two classifiers were tested, a feedforward neural network and a deeply stacked autoencoder, with the second achieving better results, correctly classifying 77% of the subjects sleep quality (either good or bad). The developed method can be implemented in a home monitoring device to estimate the sleep quality in a non-invasive way and improve the detection of pathologies.en_US
dc.languageengen_US
dc.publisher1534-4320-
dc.relation.ispartofIEEE Transactions on Neural Systems and Rehabilitation Engineeringen_US
dc.sourceIEEE Transactions on Neural Systems and Rehabilitation Engineering[ISSN 1534-4320],v. 26 (8534372), p. 2233-2239en_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherSleepen_US
dc.subject.otherElectrocardiographyen_US
dc.subject.otherElectroencephalographyen_US
dc.subject.otherCouplingen_US
dc.subject.otherREMen_US
dc.subject.otherCAPen_US
dc.titleSleep Quality Estimation by Cardiopulmonary Coupling Analysisen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TNSRE.2018.2881361en_US
dc.identifier.scopus85056582065-
dc.identifier.isi000452440100001-
dc.contributor.authorscopusid57195946416-
dc.contributor.authorscopusid55489640900-
dc.contributor.authorscopusid57200602527-
dc.contributor.authorscopusid9634135600-
dc.description.lastpage2239en_US
dc.identifier.issue8534372-
dc.description.firstpage2233en_US
dc.relation.volume26en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid6442981-
dc.contributor.daisngid4069296-
dc.contributor.daisngid1189663-
dc.contributor.daisngid1986395-
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Mendonca, F-
dc.contributor.wosstandardWOS:Mostafa, SS-
dc.contributor.wosstandardWOS:Morgado-Dias, F-
dc.contributor.wosstandardWOS:Ravelo-Garcia, AG-
dc.date.coverdateDiciembre 2018en_US
dc.identifier.ulpgces
dc.description.sjr0,992
dc.description.jcr3,478
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-8512-965X-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameRavelo García, Antonio Gabriel-
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