Automated detection of asynchrony in patient-ventilator interaction.

Qestra Mulqueeny, Stephen J. Redmond, Didier Tassaux, Laurence Vignaux, Philippe Jolliet, Piero Ceriana, Stefano Nava, Klaus Schindhelm, Nigel H. Lovell

Research output: Contribution to journalArticlepeer-review


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.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics


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