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dc.contributor.authorGutiérrez Tobal, Gonzalo César
dc.contributor.authorFrutos Arribas, Julio Fernando de 
dc.contributor.authorÁlvarez González, Daniel
dc.contributor.authorVaquerizo Villar, Fernando 
dc.contributor.authorBarroso García, Verónica 
dc.contributor.authorCrespo Senado, Andrea
dc.contributor.authorCampo Matias, Félix del 
dc.contributor.authorHornero Sánchez, Roberto 
dc.date.accessioned2018-09-03T10:54:21Z
dc.date.available2018-09-03T10:54:21Z
dc.date.issued2017
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/31355
dc.descriptionProducción Científicaes
dc.description.abstractIn the sleep apnea-hypopnea syndrome (SAHS) context, airflow signal plays a key role for the simplification of the diagnostic process. It is measured during the standard diagnostic test by the acquisition of two simultaneous sensors: a nasal prong pressure (NPP) and a thermistor (TH). The current study focuses on the comparison of their spectral content to help in the automatic SAHS-severity estimation. The spectral analysis of 315 NPP and corresponding TH recordings is firstly proposed to characterize the conventional band of interest for SAHS (0.025-0.050 Hz.). A magnitude squared coherence analysis is also conducted to quantify possible differences in the frequency components of airflow from both sensors. Then, a feature selection stage is implemented to assess the relevance and redundancy of the information extracted from the spectrum of NPP and TH airflow. Finally, a multiclass Bayesian multi-layer perceptron (BY-MLP) was used to perform an automatic estimation of SAHS severity (no-SAHS, mild, moderate, and severe), by the use of the selected spectral features from: airflow NPP alone, airflow TH alone, and both sensors jointly. The highest diagnostic performance was reached by BY-MLP only trained with NPP spectral features, reaching Cohen’s  = 0.498 in the overall four-class classification task. It also achieved 91.3%, 84.9%, and 83.3% of accuracy in the binary evaluation of the 3 apnea-hypopnea index cut-offs (5, 15, and 30 events/hour) that define the four SAHS degrees. Our results suggest that TH sensor might be not necessary for SAHS severity estimation if an automatic comprehensive characterization approach is adopted to simplify the diagnostic processes
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses
dc.titleA Bayesian Neural Network Approach to Compare the Spectral Information from Nasal Pressure and Thermistor Airflow in the Automatic Sleep Apnea Severity Estimationes
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.title.event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Societyes
dc.description.projectThis research was supported by the projects 158/2015 of “Sociedad Española de Neumología y Cirugía Torácica”, TEC2014-53196-R of "Ministerio de Economía y Competitividad (MINECO)" and FEDER, and VA037U16 of "Consejería de Educación de la Junta de Castilla y León”. F. Vaquerizo-Villar is granted with the project PEJ-2014-P-00349 from MINECO and the University of Valladolid. G. C. Gutiérrez-Tobal, V. Barroso-García, F. Vaquerizo-Villar, and R. Hornero, are with the Biomedical Engineering Group, Universidad de Valladolid, Spain (e-mail: gonzalo.gutierrez@gib.tel.uva.es). J. de Frutos, D. Álvarez, Andrea Crespo, and F. del Campo are with the Hospital Universitario Río Hortega of Valladolid, Spain (e-mail: fsas@telefonica.net).es


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