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.
Giraldo
B
B
Dep. of ESAII, Centre for Biomedical Engineering Research, Tecnical University of Catalonia, Barcelona, Spain. Beatriz.Giraldo@upc.edu
Garde
A
A
Arizmendi
C
C
Jańe
R
R
Benito
S
S
Diaz
I
I
Ballesteros
D
D
D000465:Algorithms; D016000:Cluster Analysis; D003661:Decision Support Techniques; D004867:Equipment Design; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D015203:Reproducibility of Results; D012119:Respiration; D012121:Respiration, Artificial; D012131:Respiratory Insufficiency / Q000628:therapy*; D012984:Software; D013997:Time Factors; D015300:Ventilator Weaning / Q000295:instrumentation / Q000379:methods*; D014939:Work of Breathing