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dc.contributor.authorChaparro Preciado, Javier
dc.contributor.authorGiraldo Giraldo, Beatriz
dc.contributor.authorCaminal Magrans, Pere
dc.contributor.authorBenito Vales, Salvador
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor.otherInstitut de Bioenginyeria de Catalunya
dc.date.accessioned2012-03-07T12:18:52Z
dc.date.available2012-03-07T12:18:52Z
dc.date.created2011
dc.date.issued2011
dc.identifier.citationChaparro, J. [et al.]. Analysis of the respiratory pattern variability of patients in weaning process using autoregressive modeling techniques. A: IEEE Engineering in Medicine and Biology Society. "Proceedings of the 33rd Annual International Conference of the IEEE EMBS". Boston: 2011, p. 5690-5693.
dc.identifier.isbn978-1-4244-4122-8
dc.identifier.urihttp://hdl.handle.net/2117/15505
dc.description.abstractOne of the most challenging problems in intensive care is the process of discontinuing mechanical ventilation, called weaning process. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This paper proposes to analysis the respiratory pattern variability of these patients using autoregressive modeling techniques: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). A total of 153 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S); 38 patients that failed to maintain spontaneous breathing(group F), and 21 patients who had successful weaning trials,but required reintubation in less than 48 h (group R). The respiratory pattern was characterized by their time series. The results show that significant differences were obtained with parameters as model order and first coefficient of AR model, and final prediction error by ARMA model. An accuracy of 86% (84% sensitivity and 86% specificity) has been obtained when using order model and first coefficient of AR model, and mean of breathing duration.
dc.format.extent4 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
dc.titleAnalysis of the respiratory pattern variability of patients in weaning process using autoregressive modeling techniques
dc.typeConference report
dc.contributor.groupUniversitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics
dc.rights.accessRestricted access - publisher's policy
drac.iddocument8952114
dc.description.versionPostprint (published version)
upcommons.citation.authorChaparro, J.; Giraldo, B. F.; Caminal, P.; Benito, S.
upcommons.citation.contributorIEEE Engineering in Medicine and Biology Society
upcommons.citation.pubplaceBoston
upcommons.citation.publishedtrue
upcommons.citation.publicationNameProceedings of the 33rd Annual International Conference of the IEEE EMBS
upcommons.citation.startingPage5690
upcommons.citation.endingPage5693


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