by Keyword: Autoregressive moving average models
Chaparro, J.A., Giraldo, B.F., Caminal, P., Benito, S., (2012). Performance of respiratory pattern parameters in classifiers for predict weaning process Engineering in Medicine and Biology Society (EMBC) 
34th Annual International Conference of the IEEE , IEEE (San Diego, USA) , 4349-4352
Weaning trials process of patients in intensive care units is a complex clinical procedure. 153 patients under extubation process (T-tube test) were studied: 94 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 21 patients with successful test but that had to be reintubated before 48 hours (group R). The respiratory pattern of each patient was characterized through the following time series: inspiratory time (TI), expiratory time (TE), breathing cycle duration (TTot), tidal volume (VT), inspiratory fraction (TI/TTot), half inspired flow (VT/TI), and rapid shallow index (f/VT), where f is respiratory rate. Using techniques as autoregressive models (AR), autoregressive moving average models (ARMA) and autoregressive models with exogenous input (ARX), the most relevant parameters of the respiratory pattern were obtained. We proposed the evaluation of these parameters using classifiers as logistic regression (LR), linear discriminant analysis (LDA), support vector machines (SVM) and classification and regression tree (CART) to discriminate between patients from groups S, F and R. An accuracy of 93% (98% sensitivity and 82% specificity) has been obtained using CART classification.
JTD Keywords: Accuracy, Indexes, Logistics, Regression tree analysis, Support vector machines, Time series analysis, Autoregressive moving average processes, Medical signal processing, Pattern classification, Pneumodynamics, Regression analysis, Sensitivity, Signal classification, Support vector machines, Time series, SVM, T-tube testing, Autoregressive models-with-exogenous input, Autoregressive moving average models, Breathing cycle duration, Classification-and-regression tree, Expiratory time, Extubation process, Half inspired flow, Inspiratory fraction, Inspiratory time, Intensive care units, Linear discriminant analysis, Logistic regression, Rapid shallow index, Respiratory pattern parameter performance, Sensitivity, Spontaneous breathing, Support vector machines, Tidal volume, Time 48 hr, Time series, Weaning process classifiers