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dc.contributor.authorBarroso García, Verónica 
dc.contributor.authorGutierrez Tobal, Gonzalo César 
dc.contributor.authorKheirandish Gozal, Leila
dc.contributor.authorÁlvarez González, Daniel 
dc.contributor.authorVaquerizo Villar, Fernando 
dc.contributor.authorNúñez Novo, Pablo 
dc.contributor.authorCampo Matias, Félix del 
dc.contributor.authorGozal, David
dc.contributor.authorHornero Sánchez, Roberto 
dc.date.accessioned2026-01-14T10:49:15Z
dc.date.available2026-01-14T10:49:15Z
dc.date.issued2020-01
dc.identifier.citationComputer Methods and Programs in Biomedicine, Enero 2020, vol. 183, p. 105083es
dc.identifier.issn0169-2607es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/81493
dc.descriptionProducción Científicaes
dc.description.abstractBackground and objective: In-laboratory overnight polysomnography (PSG) is the gold standard method to diagnose the Sleep Apnoea-Hypopnoea Syndrome (SAHS). PSG is a complex, expensive, labour-intensive and time-consuming test. Consequently, simplified diagnostic methods are desirable. We propose the analysis of the airflow (AF) signal by means of recurrence plots (RP) features. The main goal of our study was to evaluate the utility of the information from RPs of the AF signals to detect paediatric SAHS at different levels of severity. In addition, we also evaluated the complementarity with the 3% oxygen desaturation index (ODI3). Methods: 946 AF and blood oxygen saturation (SpO2) recordings from children ages 0–13 years were used. The population under study was randomly split into training (60%) and test (40%) sets. RP was computed and 9 RP features were extracted from each AF recording. ODI3 was also calculated from each SpO2 recording. A feature selection stage was conducted in the training group by means of the fast correlation-based filter (FCBF) methodology to obtain a relevant and non-redundant optimum feature subset. A multi-layer perceptron neural network with Bayesian approach (BY-MLP), trained with these optimum features, was used to estimate the apnoea–hypopnoea index (AHI). Results: 8 of the RP features showed statistically significant differences (p-value <0.01) among the SAHS severity groups. FCBF selected the maximum length of the diagonal lines from RP, as well as the ODI3. Using these optimum features, the BY-MLP model achieved 83.2%, 78.5%, and 91.0% accuracy in the test group for the AHI thresholds 1, 5, and 10 events/h, respectively. Moreover, this model reached a negative likelihood ratio of 0.1 for 1 event/h and a positive likelihood ratio of 13.7 for 10 events/h. Conclusions: RP analysis enables extraction of useful SAHS-related information from overnight AF paediatric recordings. Moreover, it provides complementary information to the widely-used clinical variable ODI3. Thus, RP applied to AF signals can be used along with ODI3 to help in paediatric SAHS diagnosis, particularly to either confirm the absence of SAHS or the presence of severe SAHS.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherElsevieres
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses
dc.subject.classificationAirflow (AF)es
dc.subject.classificationChildrenes
dc.subject.classificationRecurrence plots (RP)es
dc.subject.classificationSleep Apnoea-Hypopnoea Syndrome (SAHS)es
dc.titleUsefulness of recurrence plots from airflow recordings to aid in paediatric sleep apnoea diagnosises
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2019 Elsevier B.V. All rights reservedes
dc.identifier.doi10.1016/j.cmpb.2019.105083es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0169260719303980?via%3Dihubes
dc.identifier.publicationfirstpage105083es
dc.identifier.publicationtitleComputer Methods and Programs in Biomedicinees
dc.identifier.publicationvolume183es
dc.peerreviewedSIes
dc.type.hasVersioninfo:eu-repo/semantics/draftes
dc.subject.unesco1203.04 Inteligencia Artificiales
dc.subject.unesco3325.82 Procesado de señales
dc.subject.unesco3314 Tecnología Médicaes


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