DSpace DSpace UPC
 Català   Castellano   English  

E-prints UPC >
Altres >
Enviament des de DRAC >

Empreu aquest identificador per citar o enllaçar aquest ítem: http://hdl.handle.net/2117/15505

Ítem no disponible en accés obert per política de l'editorial

Arxiu Descripció MidaFormat
Y2011_BGiraldo_IEEE_EMBC.pdf121,06 kBAdobe PDF Accés restringit

Citació: 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.
Títol: Analysis of the respiratory pattern variability of patients in weaning process using autoregressive modeling techniques
Autor: Chaparro Preciado, Javier; Giraldo Giraldo, Beatriz Veure Producció científica UPC; Caminal Magrans, Pere Veure Producció científica UPC; Benito Vales, Salvador
Data: 2011
Tipus de document: Conference report
Resum: 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.
ISBN: 978-1-4244-4122-8
URI: http://hdl.handle.net/2117/15505
Apareix a les col·leccions:Altres. Enviament des de DRAC
SISBIO - Senyals i Sistemes Biomèdics. Ponències/Comunicacions de congressos
Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial. Ponències/Comunicacions de congressos
Comparteix:


Stats Mostra les estadístiques d'aquest ítem

SFX Query

Aquest ítem (excepte textos i imatges no creats per l'autor) està subjecte a una llicència de Creative Commons Llicència Creative Commons
Creative Commons

 

Valid XHTML 1.0! Programari DSpace Copyright © 2002-2004 MIT and Hewlett-Packard Comentaris
Universitat Politècnica de Catalunya. Servei de Biblioteques, Publicacions i Arxius