Support Vector Machine Classification Applied on Weaning Trials Patients

One of the most frequent reasons for instituting mechanical ventilation is to decrease patient´s work of breathing. Many attempts have been made to increase the effectiveness of the evaluation of the respiratory pattern with the analysis of the respiratory signals. This work proposes a method for the study of the differences in respiratory pattern variability in patients on weaning trials. The proposed method is based on a support vector machine using 35 features extracted from the respiratory flow signal. In this paper, a group of 146 patients with mechanical ventilation were studied: group S of 79 patients with successful weaning trials and group F of 67 patients that failed to maintain spontaneous breathing and were reconnected. Applying a feature selection procedure based on the use of the support vector machine with a leave-one-out cross-validation, it was obtained 86.67% of well classified patients on group S and 73.34% on group F, using only 8 of the 35 features. Therefore, support vector machine can be a classification method of the respiratory pattern variability useful in the study of patients on weaning trials