Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/35334
DC FieldValueLanguage
dc.contributor.authorMartín-González, Sofíaen_US
dc.contributor.authorNavarro-Mesa, Juan L.en_US
dc.contributor.authorJuliá Serdá, José Gabrielen_US
dc.contributor.authorKraemer, Jan F.en_US
dc.contributor.authorWessel, Nielsen_US
dc.contributor.authorRavelo-García, Antonio G.en_US
dc.date.accessioned2018-04-09T08:32:03Z-
dc.date.available2018-04-09T08:32:03Z-
dc.date.issued2017en_US
dc.identifier.issn0010-4825en_US
dc.identifier.urihttp://hdl.handle.net/10553/35334-
dc.description.abstractWe introduce a sleep apnea characterization and classification approach based on a Heart Rate Variability (HRV) feature selection process, thus focusing on the characterization of the underlying process from a cardiac rate point of view. Therefore, we introduce linear and nonlinear variables, namely Cepstrum Coefficients (CC), Filterbanks (Fbank) and Detrended Fluctuation Analysis (DFA). Logistic Regression, Linear Discriminant Analysis and Quadratic Discriminant Analysis were used for classification purposes.The experiments were carried out using two databases. We achieved a per-segment accuracy of 84.76\\% (sensitivity = 81.45\\%, specificity = 86.82\\%, auc = 0.92) in the Apnea-ECG Physionet database, whereas in the HuGCDN2014 database, provided by the Dr. Negrin University Hospital (Las Palmas de Gran Canaria, Spain), the best results were: accuracy = 81.96\\%, sensitivity = 70.95\\%, specificity = 85.47\\%, AUC = 0.87. The former results were comparable or better than those obtained by other methods for the same database in the recent literature.We have concluded that the selected features that best characterize the underlying process are common to both databases. This supports the fact that the conclusions reached are potentially generalizable. The best results were obtained when the three kinds of features were jointly used. Another notable fact is the small number of features needed to describe the phenomenon. Results suggest that the two first Fbanks, the first CC and the first DFA coefficient are the variables that best describe the RR pattern in OSA and, therefore, are especially relevant to extract discriminative information for apnea screening purposes.en_US
dc.languageengen_US
dc.relation.ispartofComputers in biology and medicineen_US
dc.sourceComputers in Biology and Medicine[ISSN 0010-4825],v. 91, p. 47-58en_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherSleep apneaen_US
dc.subject.otherSingle-lead ECGen_US
dc.subject.otherHeart rate variabilityen_US
dc.subject.otherCepstrumen_US
dc.subject.otherFilter banken_US
dc.subject.otherDetrended fluctuation analysisen_US
dc.subject.otherFeature selectionen_US
dc.titleHeart rate variability feature selection in the presence of sleep apnea: An expert system for the characterization and detection of the disorderen_US
dc.typeinfo:eu-repo/semantics/Articlees
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticlees
dc.identifier.doi10.1016/j.compbiomed.2017.10.004
dc.identifier.scopus85034092602
dc.identifier.isi000417660400005-
dc.contributor.authorscopusid16069177700
dc.contributor.authorscopusid9634488300
dc.contributor.authorscopusid6603171553
dc.contributor.authorscopusid52063658400
dc.contributor.authorscopusid7005373972
dc.contributor.authorscopusid9634135600
dc.identifier.eissn1879-0534-
dc.description.lastpage58-
dc.description.firstpage47-
dc.relation.volume91-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid4774892
dc.contributor.daisngid2630721
dc.contributor.daisngid2942583
dc.contributor.daisngid29068494
dc.contributor.daisngid188715
dc.contributor.daisngid1986395
dc.contributor.wosstandardWOS:Martin-Gonzalez, S
dc.contributor.wosstandardWOS:Navarro-Mesa, JL
dc.contributor.wosstandardWOS:Julia-Serda, G
dc.contributor.wosstandardWOS:Kraemer, JF
dc.contributor.wosstandardWOS:Wessel, N
dc.contributor.wosstandardWOS:Ravelo-Garcia, AG
dc.date.coverdateDiciembre 2017
dc.identifier.ulpgces
dc.description.sjr0,591
dc.description.jcr2,115
dc.description.sjrqQ2
dc.description.jcrqQ2
item.fulltextSin texto completo-
item.grantfulltextnone-
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.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-5001-9223-
crisitem.author.orcid0000-0003-3860-3424-
crisitem.author.orcid0000-0002-8512-965X-
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.fullNameMartín González, Sofía Isabel-
crisitem.author.fullNameNavarro Mesa, Juan Luis-
crisitem.author.fullNameRavelo García, Antonio Gabriel-
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