• Carlos Arizmendi, Enrique Romero, René Alquezar, Pere Caminal, Ivan Díaz, Salvador Benito, and Beatriz F Giraldo.
• Department of LSI, Technical University of Catalonia (UPC), C. Jordi Girona, 1-3, 08034, Barcelona, Spain. Carlos.Julio.Arizmendi@upc.edu
• Conf Proc IEEE Eng Med Biol Soc. 2009 Jan 1; 2009: 4343-6.

AbstractThe process of weaning from mechanical ventilation is one of the challenges in intensive care. 149 patients under extubation process (T-tube test) were studied: 88 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 23 patients with successful test but that had to be reintubated before 48 hours (group R). Each patient was characterized using 8 time series and 6 statistics extracted from respiratory and cardiac signals. A moving window statistical analysis was applied obtaining for each patient a sequence of patterns of 48 features. Applying a cluster analysis two groups with the majority dataset were obtained. Neural networks were applied to discriminate between patients from groups S, F and R. The best performance obtained was 84.0% of well classified patients using a linear perceptron trained with a feature selection procedure (that selected 19 of the 48 features) and taking as input the main cluster centroid. However, the classification baseline 69.8% could not be improved when using the original set of patterns instead of the centroids to classify the patients.

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