Identification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning

Anthony Stell, Ernesto Caparo, Zhe Wang, Chenyang Wang, David Berlowitz, Mark Howard, Richard Sinnott, Uwe Aickelin

2024

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 the 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.

Paper Citation

in Harvard Style

Stell A., Caparo E., Wang Z., Wang C., Berlowitz D., Howard M., Sinnott R. and Aickelin U. (2024). Identification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 436-443. DOI: 10.5220/0012366700003657

in Bibtex Style

@conference{healthinf24,
author={Anthony Stell and Ernesto Caparo and Zhe Wang and Chenyang Wang and David Berlowitz and Mark Howard and Richard Sinnott and Uwe Aickelin},
title={Identification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={436-443},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012366700003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Identification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning
SN - 978-989-758-688-0
AU - Stell A.
AU - Caparo E.
AU - Wang Z.
AU - Wang C.
AU - Berlowitz D.
AU - Howard M.
AU - Sinnott R.
AU - Aickelin U.
PY - 2024
SP - 436
EP - 443
DO - 10.5220/0012366700003657
PB - SciTePress