TY - DATA T1 - Simulated data from A Model-based Approach to Generating Annotated Pressure Support Waveforms PY - 2024/01/31 AU - Anouk van Diepen AU - Tom Bakkes AU - Ashley de Bie AU - Simona Turco AU - Arthur Bouwman AU - Pierre Woerlee AU - Massimo Mischi UR - DO - 10.4121/81220350-0f3d-4e0a-86cf-28c904a1cb09.v1 KW - Patient-ventilator interactions KW - Asynchronies KW - Mechanical ventilation KW - Model based methods KW - Machine learning N2 -
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.
[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
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