Design of a Classifier to Determine the Optimal Moment of Weaning of Patients undergoing to the T-tube Test

Weaning from mechanical ventilation in the intensive care unit is a complex and relevant clinical problem. Prolonged mechanical ventilation leads to a variety of medical complications that increase hospital stay and costs, in addition to contributing the morbidity and mortality, affecting long-term quality of life. This work presents a methodology to establish the optimal moment of extubation of a patient connected to a mechanical ventilator, submitted to the T-Tube test. 133 patients are analyzed, classified into two groups: successful group (94 patients) and failed group (39 patients). The behaviour of the respiratory function is characterized through the mean, standard deviation, kurtosis, skewness, interquartile range and coefficient of interval of the respiratory flow time series. To classify these patients, neural networks (NN) and support vector machines (SVM) classifier are used, considering time intervals of the 450s, 600s and 900s. According to the results, the best classification is obtained using the SVM. Clinical Relevance-The paper determines the optimal moment for weaning a patient connected to a mechanical ventilator using machine learning techniques.