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

doi:10.1109/IEMBS.2009.5332684

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

Istituti Clinici Scientifici Maugeri Spa – Società Benefit

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
Pages	5324-5327
Number of pages	4
DOIs	https://doi.org/10.1109/IEMBS.2009.5332684
Publication status	Published - 2009
Event	31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 - Minneapolis, MN, United States Duration: Sept 2 2009 → Sept 6 2009

Other

Other	31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
Country/Territory	United States
City	Minneapolis, MN
Period	9/2/09 → 9/6/09

ASJC Scopus subject areas

Cell Biology
Developmental Biology
Biomedical Engineering
Medicine(all)

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10.1109/IEMBS.2009.5332684

Cite this

Mulqueeny, Q., Redmond, S. J., Tassaux, D., Vignaux, L., Jolliet, P., Ceriana, P., Nava, S., Schindhelm, K., & Lovell, N. H. (2009). Automated detection of asynchrony in patient-ventilator interaction. In Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (pp. 5324-5327). [5332684] https://doi.org/10.1109/IEMBS.2009.5332684

Automated detection of asynchrony in patient-ventilator interaction. / Mulqueeny, Qestra; Redmond, Stephen J.; Tassaux, Didier et al.

Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009. 2009. p. 5324-5327 5332684.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mulqueeny, Q, Redmond, SJ, Tassaux, D, Vignaux, L, Jolliet, P, Ceriana, P, Nava, S, Schindhelm, K & Lovell, NH 2009, Automated detection of asynchrony in patient-ventilator interaction. in Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009., 5332684, pp. 5324-5327, 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, Minneapolis, MN, United States, 9/2/09. https://doi.org/10.1109/IEMBS.2009.5332684

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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.",

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Automated detection of asynchrony in patient-ventilator interaction

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