Automatic patient-ventilator asynchrony detection framework using objective asynchrony definitions

Journal article (2024)

Authors

Lars van de Kamp Demcon Life Sciences and Health, Eindhoven University of Technology
Joey Reinders Demcon Life Sciences and Health
Bram Hunnekens Demcon Life Sciences and Health
T.A.E. Oomen Eindhoven University of Technology, Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
Nathan van de Wouw Eindhoven University of Technology
Research Group
Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2024 Lars van de Kamp, Joey Reinders, Bram Hunnekens, T.A.E. Oomen, Nathan van de Wouw
DOI
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https://doi.org/10.1016/j.ifacsc.2023.100236
Detection Classification Supervised learning Mechanical ventilation Patient-ventilator asynchrony Recurrent neural networks
More Info
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Published Date

2024

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Jan-Willem van Wingerden

Abstract

Patient-ventilator asynchrony is one of the largest challenges in mechanical ventilation and is associated with prolonged ICU stay and increased mortality. The aim of this paper is to automatically detect and classify the different types of patient-ventilator asynchronies during a patient's breath using the typically available data on commercially available ventilators. This is achieved by a detection and classification framework using an objective definition of asynchrony and a supervised learning approach. The achieved detection performance of the near-real time framework on a clinical dataset is a significant improvement over current clinical practice, therewith and, this framework has the potential to significantly improve the patient comfort and treatment outcomes.