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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Analysis of linear lung models based on state-space models
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Analysis of linear lung models based on state-space models

机译：基于状态空间模型的线性肺模型分析

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Background and Objectives: Linear parametric respiratory system models have been used in the model-based analysis of the respiratory system. Although there are studies exploring the physiological correctness and fitting accuracy of the models, they are not analysed in terms of interaction between parameters and dynamics of the model. In this study we propose to use state-space modelling to yield the time-varying nature of the system incorporated by the parameters. Methods: We tested controllability, observability and stability characteristics of the equation of motion, 2-comp. parallel, 2-comp. series, viscoelastic, 6-element and mead models while using the parameters given in the literature. In the sensitivity analysis we proposed to use dual Desensitized Linear Kalman Filter (DKF) and Extended Kalman Filter (EKF) method. In this method, state error covariance revealed the parameter sensitivities for each model. Results: Results showed that all models, except 2-comp. parallel and mead models, are both controllable and observable models. On the other hand all models, except mead model, are stable models. Regarding to the sensitivity analysis, dual DKF - EKF method estimated states of the models successfully with a low estimation error. Sensitivity analysis results showed that airway parameters have higher effects on the state estimation than the other parameters have. Conclusion: We proved that state-space evaluation of the previously proposed parametric models of the respiratory system led us to quantitative and qualitative assessments of the respiratory models. Moreover parameter values found in the literature have different effects on the models. (C) 2019 Elsevier B.V. All rights reserved.
机译：背景和目标：线性参数呼吸系统模型已用于呼吸系统的模型分析。虽然有研究探索了模型的生理正确性和拟合精度，但它们在模型参数与动态之间的相互作用方面未分析。在这项研究中，我们建议使用状态空间建模来产生由参数的系统的时变性。方法：我们测试了运动方程的可控性，可观察性和稳定性特征，2-Comp。平行，2-comp。使用文献中给出的参数时系列，粘弹性，6元和米德型号。在敏感性分析中，我们提出使用双偏敏线性卡尔曼滤波器（DKF）和扩展卡尔曼滤波器（EKF）方法。在此方法中，状态错误协方差显示每个模型的参数敏感性。结果：结果表明，除2-comp外，所有型号。平行和米德型号既可控和可观察的型号。另一方面，除米德模型外，所有型号都是稳定的型号。关于灵敏度分析，双DKF - EKF方法成功估计了模型的状态，以低估计误差。敏感性分析结果表明，气道参数对状态估计具有比其他参数更高的效果。结论：我们证明了先前提出的呼吸系统参数模型的状态空间评估导致我们对呼吸模型的定量和定性评估。此外，文献中的参数值对模型具有不同的影响。 （c）2019年Elsevier B.V.保留所有权利。

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