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dc.contributor.authorÁlvarez, Daniel
dc.contributor.authorCrespo, Andrea
dc.contributor.authorVaquerizo-Villar, Fernando
dc.contributor.authorGutiérrez-Tobal, Gonzalo C
dc.contributor.authorCerezo-Hernández, Ana
dc.contributor.authorBarroso-García, Verónica
dc.contributor.authorAnsermino, , J Mark
dc.contributor.authorDumont, Guy A
dc.contributor.authorHornero, Roberto
dc.contributor.authordel Campo, Félix
dc.contributor.authorGarde, Ainara
dc.date.accessioned2025-01-20T17:15:10Z
dc.date.available2025-01-20T17:15:10Z
dc.date.issued2018
dc.identifier.citationPhysiological Measurement, 2018, vol. 39, p. 104002 (16pp)es
dc.identifier.issn0967-3334es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/74132
dc.descriptionProducción Científicaes
dc.description.abstractObjective: This study is aimed at assessing symbolic dynamics as a reliable technique to characterise complex fluctuations of portable oximetry in the context of automated detection of childhood obstructive sleep apnoea-hypopnoea syndrome (OSAHS). Approach: Nocturnal oximetry signals from 142 children with suspected OSAHS were acquired using the Phone Oximeter: a portable device that integrates a pulse oximeter with a smartphone. An apnoea-hypopnoea index (AHI) ⩾ 5 events h−1 from simultaneous in-lab polysomnography was used to confirm moderate-to-severe childhood OSAHS. Symbolic dynamics was used to parameterise non-linear changes in the overnight oximetry profile. Conventional indices, anthropometric measures, and time-domain linear statistics were also considered. Forward stepwise logistic regression was used to obtain an optimum feature subset. Logistic regression (LR) was used to identify children with moderate-to-severe OSAHS. Main results: The histogram of 3-symbol words from symbolic dynamics showed significant differences (p < 0.01) between children with AHI < 5 events h−1 and moderate-to-severe patients (AHI ⩾ 5 events h−1). Words representing increasing oximetry values after apnoeic events (re-saturations) showed relevant diagnostic information. Regarding the performance of individual characterization approaches, the LR model composed of features from symbolic dynamics alone reached a maximum performance of 78.4% accuracy (65.2% sensitivity; 86.8% specificity) and 0.83 area under the ROC curve (AUC). The classification performance improved combining all features. The optimum model from feature selection achieved 83.3% accuracy (73.5% sensitivity; 89.5% specificity) and 0.89 AUC, significantly (p <0.01) outperforming the other models. Significance: Symbolic dynamics provides complementary information to conventional oximetry analysis enabling reliable detection of moderate-to-severe paediatric OSAHS from portable oximetry.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherIOP Publishinges
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses
dc.titleSymbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoeaes
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holderIOP Publishinges
dc.identifier.doi10.1088/1361-6579/aae2a8es
dc.relation.publisherversionhttps://iopscience.iop.org/article/10.1088/1361-6579/aae2a8/metaes
dc.identifier.publicationfirstpage104002es
dc.identifier.publicationissue10es
dc.identifier.publicationtitlePhysiological Measurementes
dc.identifier.publicationvolume39es
dc.peerreviewedSIes
dc.description.projectThis research has been partially supported by the projects DPI2017-84280-R and RTC-2015-3446-1 from Ministerio de Economía, Industria y Competitividad and European Regional Development Fund (FEDER), projects 153/2015 and 66/2016 of the Sociedad Española de Neumología y Cirugía Torácica (SEPAR), and the project VA037U16 from the Consejería de Educación de la Junta de Castilla y León and FEDER. D Álvarez was funded by a Juan de la Cierva grant IJCI-2014-22664 from the Ministerio de Economía y Competitividad. F Vaquerizo-Villar was funded by the grant ‘Ayuda para contratos predoctorales para la Formación de Profesorado Universitario (FPU)’ from the Ministerio de Educación, Cultura y Deporte (FPU16/02938). V Barroso-García received the grant ‘Ayuda para financiar la contratación predoctoral de personal investigador’ from the Consejería de Educación de la Junta de Castilla y León and the European Social Fund. J Mark Ansermino was funded by a grant from Alevea Foundation.es
dc.identifier.essn1361-6579es
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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