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

Content Provider	IEEE Xplore Digital Library
Author	Chaparro, J.A. Giraldo, B.F. Caminal, P. Benito, S.
Copyright Year	2011
Description	Author affiliation: Dept. ESAII, Universitat Politècnica de Catalunya (UPC) and CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (Caminal, P.) \|\| Dept. ESAII, Escola Universitaria Enginyeria Tècnica Industrial de Barcelona (EUETIB), Universitat Politècnica de Catalunya (UPC), Institut de Bioingenyeria de Catalunya (IBEC) and CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN). c/. Pau Gargallo, 5, 08028, Barcelona, Spain (Giraldo, B.F.) \|\| Dept. Intensive Care Medicine, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain (Benito, S.) \|\| Escuela Colombiana de Ingeniería, Bogotá, Colombia (Chaparro, J.A.)
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.
Starting Page	5690
Ending Page	5693
File Size	405086
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091377
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Autoregressive processes Support vector machines Analytical models Predictive models Logistics Ventilation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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