The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/81220350-0f3d-4e0a-86cf-28c904a1cb09

van Diepen, Anouk; Bakkes, Tom; de Bie, Ashley; Turco, Simona; Bouwman, Arthur et. al. (2024): Simulated data from A Model-based Approach to Generating Annotated Pressure Support Waveforms. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/81220350-0f3d-4e0a-86cf-28c904a1cb09.v2

Dataset

x choose version: version 1 - 2024-01-31

• Cardiorespiratory Medicine and Haematology
• Clinical Health (organs, Diseases and Abnormal Conditions)
• Health and Support Services
• Biomedical Engineering
• Engineering
• Health
• Medical and Health Sciences

Asynchronies, Machine learning, Mechanical ventilation, Model based methods, Patient-ventilator interactions

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Anouk van Diepen , Tom Bakkes , Ashley de Bie, Simona Turco, Arthur Bouwman, Pierre Woerlee, Massimo Mischi

This data was generated based on simulations of the patient-ventilator interaction in research by A. van Diepen et al. [1]. The data contains the airway pressure, flow, and volume waveforms including the labeling of patient and ventilator timings resulting from the simulations. In total, the data contains 1405 simulation runs. Subsequently, the simulated data was used in the development of patient-ventilator asynchrony detection and the evaluation of inspiratory effort estimation, in research by T.H.G.F. Bakkes et al. and A. van Diepen et al., respectively [2, 3].

 

More details on the contents of the files can be found in the 'Read me' file.

Changes 19-07-2024:

Added muscle pressure to 'waveforms.zip'

Added 'pmus' description to 'Read me.txt'

 

[1] A. van Diepen et al., A model-based approach to generating annotated pressure support waveforms, DOI: https://doi.org/10.1007/s10877-022-00822-4

[2] T.H.G.F. Bakkes et al., Automated detection and classification of patient-ventilator asynchrony by means of machine learning and simulated data, DOI: https://doi.org/10.1016/j.cmpb.2022.107333

[3] A. van Diepen et al., Evaluation of the accuracy of established patient inspiratory effort estimation methods during mechanical support ventilation, DOI: https://doi.org/10.1016/j.heliyon.2023.e13610

• 2024-01-31 first online

• 2024-07-19 published, posted

4TU.ResearchData

zip. files containing .csv, and one read me file as .txt

A model-based approach to generating annotated pressure support waveforms

• https://doi.org/10.1007/s10877-022-00822-4
• https://doi.org/10.1016/j.cmpb.2022.107333
• https://doi.org/10.1016/j.heliyon.2023.e13610

Eindhoven University of Technology, Department of Electrical Engineering