Analysis of the respiratory pattern variability of patients in weaning process using autoregressive modeling techniques
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 |
local.identifier.drac | 8952114 |
dc.description.version | Postprint (published version) |
local.citation.author | Chaparro, J.; Giraldo, B. F.; Caminal, P.; Benito, S. |
local.citation.contributor | IEEE Engineering in Medicine and Biology Society |
local.citation.pubplace | Boston |
local.citation.publicationName | Proceedings of the 33rd Annual International Conference of the IEEE EMBS |
local.citation.startingPage | 5690 |
local.citation.endingPage | 5693 |
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