A Model-based Approach to Generating Annotated Pressure Support Waveforms

Autor: A. van Diepen, T. H. G. F. Bakkes, A. J. R. De Bie, S. Turco, R. A. Bouwman, P. H. Woerlee, M. Mischi
Přispěvatelé: Biomedical Diagnostics Lab, Bayesian Intelligent Autonomous Systems, Signal Processing Systems, Eindhoven MedTech Innovation Center, Center for Care & Cure Technology Eindhoven, Electrical Engineering, NeuroPlatform, EAISI Health
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Journal of Clinical Monitoring and Computing, 36(6), 1739-1752. Springer
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 4188-4191
STARTPAGE=4188;ENDPAGE=4191;TITLE=2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
ISSN: 1387-1307
4188-4191
Popis: Large numbers of asynchronies during pressure support ventilation cause discomfort and higher work of breathing in the patient, and are associated with an increased mortality. There is a need for real-time decision support to detect asynchronies and assist the clinician towards lung-protective ventilation. Machine learning techniques have been proposed to detect asynchronies, but they require large datasets with sufficient data diversity, sample size, and quality for training purposes. In this work, we propose a method for generating a large, realistic and labeled, synthetic dataset for training and validating machine learning algorithms to detect a wide variety of asynchrony types. We take a model-based approach in which we adapt a non-linear lung-airway model for use in a diverse patient group and add a first-order ventilator model to generate labeled pressure, flow, and volume waveforms of pressure support ventilation. The model was able to reproduce basic measured lung mechanics parameters. Experienced clinicians were not able to differentiate between the simulated waveforms and clinical data (P = 0.44 by Fisher’s exact test). The detection performance of the machine learning trained on clinical data gave an overall comparable true positive rate on clinical data and on simulated data (an overall true positive rate of 94.3% and positive predictive value of 93.5% on simulated data and a true positive rate of 98% and positive predictive value of 98% on clinical data). Our findings demonstrate that it is possible to generate labeled pressure and flow waveforms with different types of asynchronies.
Databáze: OpenAIRE
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