Authors:
Anthony Stell
1
;
Ernesto Caparo
1
;
Zhe Wang
1
;
Chenyang Wang
1
;
David Berlowitz
2
;
Mark Howard
3
;
Richard Sinnott
1
and
Uwe Aickelin
1
Affiliations:
1
School of Computing and Information Systems, University of Melbourne, 700 Swanston Street, Melbourne, Australia
;
2
Institute of Breathing and Sleep, Austin Hospital, 145 Studley Road, Heidelberg, Australia
;
3
Austin Health, 145 Studley Road, Heidelberg, Australia
Keyword(s):
Patient Ventilator Asynchrony, Machine-Learning, European Data Format (EDF).
Abstract:
Patient Ventilator Asynchrony (PVA) occurs where a mechanical ventilator aiding a patient’s breathing falls out of synchronisation with their breathing pattern. This de-synchronisation may cause patient distress and can lead to long-term negative clinical outcomes. Research into the causes and possible mitigations of PVA is currently conducted by clinical domain experts using manual methods, such as parsing entire sleep hypnograms visually, and identifying and tagging instances of PVA that they find. This process is very labour-intensive and can be error prone. This project aims to make this analysis more efficient, by using machine-learning approaches to automatically parse, classify, and suggest instances of PVA for ultimate confirmation by domain experts. The solution has been developed based on a retrospective dataset of intervention and control patients that were recruited to a non-invasive ventilation study. This achieves a specificity metric of over 90%. This paper describes t
he process of integrating the output of the machine learning into the bedside clinical monitoring system for production use in anticipation of a future clinical trial.
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