Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/70470
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dc.contributor.authorMendonça, Fábioen_US
dc.contributor.authorMostafa, Sheikh Shanawazen_US
dc.contributor.authorMorgado-Dias, Fernandoen_US
dc.contributor.authorRavelo García, Antonio Gabrielen_US
dc.date.accessioned2020-02-22T06:03:49Z-
dc.date.available2020-02-22T06:03:49Z-
dc.date.issued2020en_US
dc.identifier.issn1424-8220en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/70470-
dc.description.abstractSleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate results, it is time consuming, labor-intensive, and expensive. A non-invasive and easy to self-assemble home monitoring device was developed to address these issues. The device can perform the OSA diagnosis at the patient’s home and a specialized technician is not required to supervise the process. An automatic scoring algorithm was developed to examine the blood oxygen saturation signal for a minute-by-minute OSA assessment. It was performed by analyzing statistical and frequency-based features that were fed to a classifier. Afterward, the ratio of the number of minutes classified as OSA to the time in bed in minutes was compared with a threshold for the global (subject-based) OSA diagnosis. The average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for the minute-by-minute assessment were, respectively, 88%, 80%, 91%, and 0.86. The subject-based accuracy was 95%. The performance is in the same range as the best state of the art methods for the models based only on the blood oxygen saturation analysis. Therefore, the developed model has the potential to be employed in clinical analysis.en_US
dc.languageengen_US
dc.relation.ispartofSensorsen_US
dc.sourceSensors (Switzerland) [ISSN 1424-8220], v. 20 (3)en_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherApneaen_US
dc.subject.otherHome Monitoring Deviceen_US
dc.subject.otherOsaen_US
dc.subject.otherSpo2en_US
dc.titleAn oximetry based wireless device for sleep apnea detectionen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/s20030888en_US
dc.identifier.scopus85079338328-
dc.contributor.authorscopusid57195946416-
dc.contributor.authorscopusid55489640900-
dc.contributor.authorscopusid57200602527-
dc.contributor.authorscopusid9634135600-
dc.identifier.issue3-
dc.relation.volume20en_US
dc.investigacionCiencias de la Saluden_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.identifier.ulpgcen_US
dc.description.sjr0,636
dc.description.jcr3,576
dc.description.sjrqQ2
dc.description.jcrqQ2
dc.description.scieSCIE
item.fulltextCon texto completo-
item.grantfulltextopen-
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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