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集中式动脉系统模型的在线参数辨识:一项仿真研究。

On-line parameter identification of the lumped arterial system model: A simulation study.

机构信息

College of Metrology & Measurement Engineering, China Jiliang University, Hangzhou, China.

The Affiliated Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, China.

出版信息

PLoS One. 2020 Jul 10;15(7):e0236012. doi: 10.1371/journal.pone.0236012. eCollection 2020.

Abstract

A lumped model of the arterial system has been used in constructing a hybrid mock loop due to its real-time response. However, the parameters of the model are always from a general case and not adapted to a specific patient. In this study, we focused on on-line parameter identification of the lumped model of the arterial system that could be used for a specific patient. A five-element lumped arterial model is adopted in this study, in which the five parameters are to be determined. The aortic flow rate and the venous pressure are chosen as the inputs of the model, and aortic pressure as the output. An iterative optimization based on the established state space equations of the five-element model is used to seek the best parameter values by minimizing the difference between the model prediction and the previously obtained aortic pressure. The method is validated using simulated data from a complete numerical cardiovascular model. Results show that the method can track the dynamic variation of the parameters very well. Finally, a sensitivity analysis of the model parameters is conducted to interpret the effect of parameter changes. The good performance of the identification demonstrates the potential application of this method to customize a hybrid mock loop for a specific patient or clinically monitor the arterial vessel characteristics in real time.

摘要

由于实时响应,在构建混合模拟回路时使用了动脉系统的集中模型。然而,模型的参数通常来自一般情况,并不适用于特定患者。在这项研究中,我们专注于可用于特定患者的动脉系统集中模型的在线参数识别。本研究采用了五元件集中动脉模型,其中需要确定五个参数。选择主动脉流量和静脉压力作为模型的输入,主动脉压力作为输出。基于建立的五元件模型的状态空间方程,通过最小化模型预测与先前获得的主动脉压力之间的差异,使用基于迭代的优化方法来寻求最佳参数值。使用完整的数值心血管模型生成的模拟数据对该方法进行了验证。结果表明,该方法可以很好地跟踪参数的动态变化。最后,对模型参数进行了敏感性分析,以解释参数变化的影响。识别的良好性能表明该方法有可能为特定患者定制混合模拟回路或实时临床监测动脉血管特征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c88/7351215/4cc603921a9b/pone.0236012.g001.jpg

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