Simulated data from A Model-based Approach to Generating Annotated Pressure Support Waveforms
收藏资源简介：
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.<br>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
本数据集基于A. van Diepen等人[1]的研究中患者-呼吸机交互（patient-ventilator interaction）模拟生成。数据集包含气道压力、流量及容积波形，并标注了模拟产生的患者与呼吸机时序信息。总计包含1405次模拟运行。后续，该模拟数据分别被应用于T.H.G.F. Bakkes等人[2]开展的患者-呼吸机不同步（patient-ventilator asynchrony）检测研究，以及A. van Diepen等人[3]开展的吸气努力估计（inspiratory effort estimation）方法评估研究。关于文件内容的更多细节可参阅"Read me"文件。 2024年7月19日更新：向"waveforms.zip"中新增肌肉压力（muscle pressure）数据；在"Read me.txt"中补充"pmus"相关说明。 [1] A. van Diepen et al., 基于模型的带标注压力支持波形生成方法，DOI: https://doi.org/10.1007/s10877-022-00822-4 [2] T.H.G.F. Bakkes et al., 基于机器学习与模拟数据的患者-呼吸机不同步自动检测与分类，DOI: https://doi.org/10.1016/j.cmpb.2022.107333 [3] A. van Diepen et al., 机械通气期间现有患者吸气努力估计方法的准确性评估，DOI: https://doi.org/10.1016/j.heliyon.2023.e13610
