Patients on weaning trials from mechanical ventilation classified with neural networks and feature selection.
One of the challenges in intensive care is the process of weaning from mechanical ventilation. We studied the differences in respiratory pattern variability between patients capable of maintaining spontaneous breathing during weaning trials and patients that fail to maintain spontaneous breathing. In this work, neural networks were applied to study these differences. 64 patients from mechanical ventilation are studied: Group S with 32 patients with Successful trials and Group F with 32 patients that Failed to maintain spontaneous breathing and were reconnected. A performance of 64.56% of well classified patients was obtained using a neural network trained with the whole set of 35 features. After the application of a feature selection procedure (backward selection) 84.56% was obtained using only 8 of the 35 features.
Giraldo
B
B
Dept. of ESAII, Tech. Univ. of Catalonia, Barcelona, Spain. Beatriz.Giraldo@upc.edu
Arizmendi
C
C
Romero
E
E
Alquezar
R
R
Caminal
P
P
Benito
S
S
Ballesteros
D
D
D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012129:Respiratory Function Tests / Q000379:methods*; D012131:Respiratory Insufficiency / Q000175:diagnosis* / Q000534:rehabilitation*; D012680:Sensitivity and Specificity; D013813:Therapy, Computer-Assisted / Q000379:methods*; D015300:Ventilator Weaning / Q000379:methods*