Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/77396
Campo DC Valoridioma
dc.contributor.authorMendonça, Fábioen_US
dc.contributor.authorMostafa, Sheikh Shanawazen_US
dc.contributor.authorMorgado-Dias, Fernandoen_US
dc.contributor.authorRavelo-García, Antonio G.en_US
dc.date.accessioned2021-02-01T08:26:42Z-
dc.date.available2021-02-01T08:26:42Z-
dc.date.issued2021en_US
dc.identifier.issn0933-3657en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/77396-
dc.description.abstractThe relevance of sleep quality examination for clinical diagnosis is increasing with the discovery of new relationships with several diseases and the overall wellness. This assessment is commonly performed by conducting interviews with the subjects, evaluating the self-report and psychological variables. However, this approach has a major constraint since the subject is a poor self-observer of sleep behaviors. To address this issue, a method based on the examination of a physiological signal was developed. Specifically, the single-lead electrocardiogram signal was examined to estimate the cardiopulmonary coupling between the electrocardiogram derived respiration signal and the normal-to-normal sinus interbeat interval series. A one dimensional array was created from the coupling signal and was fed to a convolutional neural network to estimate the sleep quality. The age-related cyclic alternating pattern rate percentages in healthy subjects was considered as the classification reference. An accuracy of 91 % was attained by the developed model, with an area under the receiver operating characteristic curve of 97 %. The performance is in the upper range of the reported performance by the works presented in the state of the art, advocating the relevance of the proposed method. The model was implemented in a small field programmable gate array board. Hence, a home monitoring device was created, composed of a processing unit, a sensing module and a display unit. The device is resilient, easy to self-assemble and operate, and can conceivably be employed for clinical analysis.en_US
dc.languageengen_US
dc.relation.ispartofArtificial Intelligence in Medicineen_US
dc.sourceArtificial Intelligence in Medicine[ISSN 0933-3657],v. 112, (Febrero 2021)en_US
dc.subject.other1D-Cnnen_US
dc.subject.otherCapen_US
dc.subject.otherEcgen_US
dc.subject.otherFpgaen_US
dc.subject.otherSleep Qualityen_US
dc.titleA method based on cardiopulmonary coupling analysis for sleep quality assessment with FPGA implementationen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.artmed.2021.102019en_US
dc.identifier.scopus85099654672-
dc.contributor.authorscopusid57195946416-
dc.contributor.authorscopusid55489640900-
dc.contributor.authorscopusid57200602527-
dc.contributor.authorscopusid9634135600-
dc.identifier.eissn1873-2860-
dc.relation.volume112en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.date.coverdateFebrero 2021en_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-TELen_US
dc.description.sjr1,497
dc.description.jcr7,011
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
dc.description.miaricds11,0
item.fulltextSin texto completo-
item.grantfulltextnone-
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-
Colección:Artículos
Vista resumida

Citas SCOPUSTM   

4
actualizado el 24-mar-2024

Visitas

133
actualizado el 06-ene-2024

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.