Model order selection criterion for monitoring haemoglobin status in dengue patients using ARX model

Rahim, H. Abdul; Ibrahim, Fatimah; Taib, Mohd Nasir

doi:10.1109/itab.2008.4570537

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…9) with the best classification rate (87%), was the average value of T E variable (X 1 ), AR model order of T E variable (X 5 ) and the first AR coefficient of T E (variable X 9 ). 9 (9) The linear discriminant function (Eq. 10) with better classification rate (86%), combined the average variables T E , order p in AR model for T E and T Tot series, and the first coefficient of the model for the same series.…”

Section: Resultsmentioning

confidence: 99%

“…where p is the order of the model, N the number of data and s 2 p the total square error, which is given by [9] ¦ 1 2 2…”

Section: Modeling Techniquesmentioning

confidence: 99%

“…The power of ARMA models is that they can incorporate both autoregressive and moving average terms. The use of ARMA models was popularized by Box and Jenkins [9]. ARMA(p, q) model is given by …”

Section: Modeling Techniquesmentioning

confidence: 99%

“…where p and q are the orders of the process estimated by the Akaike criterion, and a 1, ...,a p and b 1 ,...,b q are the coefficients of the model [9].…”

Section: Modeling Techniquesmentioning

confidence: 99%

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

Chaparro

Giraldo

Caminal

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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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.

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Section: Resultsmentioning

confidence: 99%

“…where p is the order of the model, N the number of data and s 2 p the total square error, which is given by [9] ¦ 1 2 2…”

Section: Modeling Techniquesmentioning

confidence: 99%

Section: Modeling Techniquesmentioning

confidence: 99%

“…where p and q are the orders of the process estimated by the Akaike criterion, and a 1, ...,a p and b 1 ,...,b q are the coefficients of the model [9].…”

Section: Modeling Techniquesmentioning

confidence: 99%

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