title: Automated detection of asynchrony in patient-ventilator interaction.
abstract: 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.
authors: Graduate School of Biomedical Engineering, University of New South Wales, Sydney NSW 2052, Australia.
authors: Qestra
authors: Q
authors: Mulqueeny
authors: Stephen J
authors: SJ
authors: Redmond
authors: Didier
authors: D
authors: Tassaux
authors: Laurence
authors: L
authors: Vignaux
authors: Philippe
authors: P
authors: Jolliet
authors: Piero
authors: P
authors: Ceriana
authors: Stefano
authors: S
authors: Nava
authors: Klaus
authors: K
authors: Schindhelm
authors: Nigel H
authors: NH
authors: Lovell
mesh_terms: D001331:Automation / Q000379:methods*; D006801:Humans; D011312:Pressure; D015656:Respiratory Mechanics / Q000502:physiology*; D012122:Ventilators, Mechanical