Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/52452
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dc.contributor.authorRavelo-García, Antonio G.en_US
dc.contributor.authorKraemer, Jan F.en_US
dc.contributor.authorNavarro-Mesa, Juan L.en_US
dc.contributor.authorHernández-Pérez, Eduardoen_US
dc.contributor.authorNavarro-Esteva, Javieren_US
dc.contributor.authorJuliá-Serdá, Gabrielen_US
dc.contributor.authorPenzel, Thomasen_US
dc.contributor.authorWessel, Nielsen_US
dc.contributor.otherHernandez-Perez, Eduardo-
dc.contributor.otherRavelo-Garcia, Antonio G.-
dc.date.accessioned2018-11-25T20:27:53Z-
dc.date.available2018-11-25T20:27:53Z-
dc.date.issued2015en_US
dc.identifier.issn1099-4300en_US
dc.identifier.urihttp://hdl.handle.net/10553/52452-
dc.description.abstractA diagnostic system for sleep apnea based on oxygen saturation and RR intervals obtained from the EKG (electrocardiogram) is proposed with the goal to detect and quantify minute long segments of sleep with breathing pauses. We measured the discriminative capacity of combinations of features obtained from RR series and oximetry to evaluate improvements of the performance compared to oximetry-based features alone. Time and frequency domain variables derived from oxygen saturation (SpO2) as well as linear and non-linear variables describing the RR series have been explored in recordings from 70 patients with suspected sleep apnea. We applied forward feature selection in order to select a minimal set of variables that are able to locate patterns indicating respiratory pauses. Linear discriminant analysis (LDA) was used to classify the presence of apnea during specific segments. The system will finally provide a global score indicating the presence of clinically significant apnea integrating the segment based apnea detection. LDA results in an accuracy of 87%; sensitivity of 76% and specificity of 91% (AUC = 0.90) with a global classification of 97% when only oxygen saturation is used. In case of additionally including features from the RR series; the system performance improves to an accuracy of 87%; sensitivity of 73% and specificity of 92% (AUC = 0.92), with a global classification rate of 100%.en_US
dc.languageengen_US
dc.relation.ispartofEntropyen_US
dc.sourceEntropy,v. 17, p. 2932-2957en_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherSleep apneaen_US
dc.subject.otherOxygen saturationen_US
dc.subject.otherRR intervalsen_US
dc.subject.otherFeature selectionen_US
dc.titleOxygen saturation and RR intervals feature selection for sleep apnea detectionen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/e17052932en_US
dc.identifier.scopus84930079459-
dc.identifier.isi000356880500023-
dcterms.isPartOfEntropy-
dcterms.sourceEntropy[ISSN 1099-4300],v. 17 (5), p. 2932-2957-
dc.contributor.authorscopusid9634135600-
dc.contributor.authorscopusid52063658400-
dc.contributor.authorscopusid9634488300-
dc.contributor.authorscopusid9636138800-
dc.contributor.authorscopusid56006484500-
dc.contributor.authorscopusid6603171553-
dc.contributor.authorscopusid7005360676-
dc.contributor.authorscopusid7005373972-
dc.description.lastpage2957en_US
dc.description.firstpage2932en_US
dc.relation.volume17en_US
dc.type2Artículoen_US
dc.identifier.wosWOS:000356880500023-
dc.contributor.daisngid1986395-
dc.contributor.daisngid2601144-
dc.contributor.daisngid29068494-
dc.contributor.daisngid2630721-
dc.contributor.daisngid4183732-
dc.contributor.daisngid4253704-
dc.contributor.daisngid2942583-
dc.contributor.daisngid35791-
dc.contributor.daisngid188715-
dc.identifier.investigatorRIDL-3413-2017-
dc.identifier.investigatorRIDNo ID-
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Ravelo-Garcia, AG-
dc.contributor.wosstandardWOS:Kraemer, JF-
dc.contributor.wosstandardWOS:Navarro-Mesa, JL-
dc.contributor.wosstandardWOS:Hernandez-Perez, E-
dc.contributor.wosstandardWOS:Navarro-Esteva, J-
dc.contributor.wosstandardWOS:Julia-Serda, G-
dc.contributor.wosstandardWOS:Penzel, T-
dc.contributor.wosstandardWOS:Wessel, N-
dc.date.coverdateEnero 2015en_US
dc.identifier.ulpgces
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.deptIDeTIC: 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.deptIDeTIC: 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.deptIDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.orcid0000-0002-8512-965X-
crisitem.author.orcid0000-0003-3860-3424-
crisitem.author.orcid0000-0001-7473-5454-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameRavelo García, Antonio Gabriel-
crisitem.author.fullNameNavarro Mesa, Juan Luis-
crisitem.author.fullNameHernández Pérez, Eduardo-
crisitem.author.departamentoSeñales y Comunicaciones-
crisitem.author.departamentoSeñales y Comunicaciones-
crisitem.author.departamentoSeñales y Comunicaciones-
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