Show simple item record

dc.contributor.authorGutiérrez Tobal, Gonzalo César
dc.contributor.authorÁlvarez González, Daniel
dc.contributor.authorCrespo Senado, Andrea
dc.contributor.authorArroyo Domingo, Carmen Ainhoa 
dc.contributor.authorVaquerizo Villar, Fernando 
dc.contributor.authorBarroso García, Verónica 
dc.contributor.authorCampo Matias, Félix del 
dc.contributor.authorHornero Sánchez, Roberto 
dc.date.accessioned2016-12-15T07:40:10Z
dc.date.available2016-12-15T07:40:10Z
dc.date.issued2016
dc.identifier.citationMedical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), 2016 Global, Institute of Electrical and Electronics Engineers (IEEE) , 2016, p. 95-99es
dc.identifier.isbn978-1-5090-2484-1es
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/21750
dc.descriptionProducción Científicaes
dc.description.abstractThis paper aims at evaluating a novel multi-class methodology to establish Sleep Apnea-Hypopnea Syndrome (SAHS) severity by the use of single-channel at-home oximetry recordings. The study involved 320 participants derived to a specialized sleep unit due to SAHS suspicion. These were assigned to one out of the four SAHS severity degrees according to the apnea-hypopnea index (AHI): no-SAHS (AHI<5 events/hour), mild-SAHS (5≤AHI<15 e/h), moderate-SAHS (15≤AHI<30 e/h), and severe-SAHS (AHI≥30 e/h). A set of statistical, spectral, and non-linear features were extracted from blood oxygen saturation (SpO2) signals to characterize SAHS. Then, an optimum set among these features were automatically selected based on relevancy and redundancy analyses. Finally, a multi-class AdaBoost model, built with the optimum set of features, was obtained from a training set (60%) and evaluated in an independent test set (40%). Our AdaBoost model reached 0.386 Cohen’s kappa in the four-class classification task. Additionally, it reached accuracies of 89.8%, 85.8%, and 74.8% when evaluating the AHI thresholds 5 e/h, 15 e/h, and 30 e/h, respectively, outperforming the classic oxygen desaturation index. Our results suggest that SpO2 obtained at home, along with multi-class AdaBoost, are useful to detect SAHS severity.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)es
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectOximetryes
dc.titleMulti-Class AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome Severity from Oximetry Recordings Obtained at Homees
dc.typeinfo:eu-repo/semantics/bookPartes
dc.relation.publisherversionhttps://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7500953es
dc.description.projectJunta de Castilla y León (project VA059U13)es
dc.description.projectPneumology and Thoracic Surgery Spanish Society (265/2012)es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record