Analysis of the respiratory pattern variability of patients in weaning process using autoregressive modeling techniques

Chaparro Preciado, Javier; Giraldo Giraldo, Beatriz; Caminal Magrans, Pere; Benito Vales, Salvador

dc.contributor.author	Chaparro Preciado, Javier
dc.contributor.author	Giraldo Giraldo, Beatriz
dc.contributor.author	Caminal Magrans, Pere
dc.contributor.author	Benito Vales, Salvador
dc.contributor.other	Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor.other	Institut de Bioenginyeria de Catalunya
dc.date.accessioned	2012-03-07T12:18:52Z
dc.date.available	2012-03-07T12:18:52Z
dc.date.created	2011
dc.date.issued	2011
dc.identifier.citation	Chaparro, 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.isbn	978-1-4244-4122-8
dc.identifier.uri	http://hdl.handle.net/2117/15505
dc.description.abstract	One 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.extent	4 p.
dc.language.iso	eng
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Ciències de la salut
dc.subject	Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
dc.title	Analysis of the respiratory pattern variability of patients in weaning process using autoregressive modeling techniques
dc.type	Conference report
dc.contributor.group	Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics
dc.rights.access	Restricted access - publisher's policy
drac.iddocument	8952114
dc.description.version	Postprint (published version)
upcommons.citation.author	Chaparro, J.; Giraldo, B. F.; Caminal, P.; Benito, S.
upcommons.citation.contributor	IEEE Engineering in Medicine and Biology Society
upcommons.citation.pubplace	Boston
upcommons.citation.published	true
upcommons.citation.publicationName	Proceedings of the 33rd Annual International Conference of the IEEE EMBS
upcommons.citation.startingPage	5690
upcommons.citation.endingPage	5693