Automated detection of asynchrony in patient-ventilator interaction.
An automated classification algorithm for the detection of expiratory ineffective efforts in patient-ventilator interaction is developed and validated. Using this algorithm, 5624 breaths from 23 patients in a pulmonary ward were examined. The participants (N = 23) underwent both conventional and non-invasive ventilation. Tracings of patient flow, pressure at the airway, and transdiaphragmatic pressure were manually labeled by an expert. Overall accuracy of 94.5% was achieved with sensitivity 58.7% and specificity 98.7%. The results demonstrate the viability of using pattern classification techniques to automatically detect the presence of asynchrony between a patient and their ventilator.
Mulqueeny
Qestra
Q
Graduate School of Biomedical Engineering, University of New South Wales, Sydney NSW 2052, Australia.
Redmond
Stephen J
SJ
Tassaux
Didier
D
Vignaux
Laurence
L
Jolliet
Philippe
P
Ceriana
Piero
P
Nava
Stefano
S
Schindhelm
Klaus
K
Lovell
Nigel H
NH
D001331:Automation / Q000379:methods*; D006801:Humans; D011312:Pressure; D015656:Respiratory Mechanics / Q000502:physiology*; D012122:Ventilators, Mechanical