Parameter Identification and Comparative Verification of Different COPD Respiratory MechanicsModels Based on Constrained Optimization Method
Xu Liqiang1,2#, Qiao Huiting1,2#, Zhang Chi1#, Xia Jingen3, Li Deyu1,4#*
1(School of Biological Science and Medical Engineering, National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices, Key Laboratory of Innovation and Transformation of Advanced MedicalDevices of Ministry of Industry and Information Technology, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing 100191, China) 2(Beihang University-Beijing Aeonmed Co. Ltd. Joint Laboratory for Diagnosis and Treatment Technology of Respiratory System and Related Diseases, Beijing 100191, China) 3(Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing 100029, China) 4(State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China)
Abstract：In intelligent closed-loop ventilation control, the physiological model of the respiratory system must be consistent with the patient′s actual condition, and its parameter estimation must be real-time and effective.Using the parameter estimation method of constrained optimization, models that can reflect the pathological conditions of chronic obstructive pulmonary disease (COPD) patients as well as can be used for real-time estimation were compared and verified. In this study, we used numerical simulation data and clinical data to estimate parameters of respiratory mechanics models with different complexities. The estimated results were used to reconstruct airway pressure. By comparing the estimation results and reconstruction results among different models, the mathematical description of respiratory mechanics problems suitable for mechanical ventilation COPD was selected. For four models with the different complexities, the corresponding parameter estimation methods were established. By using the simulation and clinical data, a constrained optimization parameter estimation method was applied to different models. Comparative analysis between the reconstructed airway pressure (using estimated resistance and elasticity) and input airway pressure demonstrated that the elastic nonlinear and resistance nonlinear models exhibited the smallest root mean square error (RMSE) in simulation data. For the 12 respiratory cycle data of 6 clinical cases, the resistance nonlinear model achieved the lowest RMSE (M=1.55 cmH2O, SD=0.69 cmH2O) among the evaluated models. The multiple comparison results of repeated measures ANOVA showed that the resistance nonlinear model statistically significantly outperformed the other models (P<0.05). The results showed that according to the simulation data and clinical data in this study, the resistance nonlinear model was more appropriate model of ventilated COPD. Based on the application of constraint optimization method for real-time parameter estimation of mechanical ventilation, the resistance nonlinear model reflected the respiratory mechanics characteristics of COPD better. The results can be used for monitoring respiratory mechanics of COPD, and also provide a basis for the intelligent parameter setting and setting of ventilators.
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