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Conf Proc IEEE Eng Med Biol Soc. 2009;2009:5324-7. doi: 10.1109/IEMBS.2009.5332684.

Automated detection of asynchrony in patient-ventilator interaction.

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Graduate School of Biomedical Engineering, University of New South Wales, Sydney NSW 2052, Australia.


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.

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