Simulation of spontaneous breathing for healthy adults using a nonlinear airway-segmented model of respiratory mechanics_Journal of Biomedical Engineering

Authors：

LIU Tianya ¹ , QIAO Huiting ¹ , LI Deyu ¹ ,  FAN Yubo ^1,2

1. Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, P.R.China;
2. Beijing Key Laboratory of Rehabilitation Engineering for Elderly, National Research Center for Rehabilitation Technical Aids, Beijing 100176, P.R.China;

Corresponding author：

FAN Yubo, Email: yubofan@buaa.edu.cn

Keywords：

respiratory system; respiratory mechanics; spontaneous breathing; airway-segmented model

DOI：

10.7507/1001-5515.201806007

Video：

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Abstract Full text Figures/Tables Video References Cited by

One-compartment lumped-parameter models of respiratory mechanics, representing the airflow resistance of the tracheobronchial tree with a linear or nonlinear resistor, are not able to describe the mechanical property of airways in different generations. Therefore, based on the anatomic structure of tracheobronchial tree and the mechanical property of airways in each generation, this study classified the human airways into three segments: the upper airway segment, the collapsible airway segment, and the small airway segment. Finally, a nonlinear, multi-compartment lumped-parameter model of respiratory mechanics with three airway segments was established. With the respiratory muscle effort as driving pressure, the model was used to simulate the tidal breathing of healthy adults. The results were consistent with the physiological data and the previously published results, suggesting that this model could be used for pathophysiological research of respiratory system.

Citation： LIU Tianya, QIAO Huiting, LI Deyu, FAN Yubo. Simulation of spontaneous breathing for healthy adults using a nonlinear airway-segmented model of respiratory mechanics. Journal of Biomedical Engineering, 2019, 36(1): 101-106. doi: 10.7507/1001-5515.201806007 Copy

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2.	Ren Shuai, Cai Maolin, Shi Yan, et al. Influence of bronchial diameter change on the airflow dynamics based on a pressure-controlled ventilation system. Int J Numer Method Biomed Eng, 2018, 34(3): e2929.
3.	Crooke P S, Hotchkiss J R, Marini J J. Linear and nonlinear mathematical models for noninvasive ventilation. Math Comput Model, 2002, 35(11/12): 1297-1313.
4.	易韦韦, 张玘, 王跃科, 等. 基于呼吸机的人工呼吸系统力学建模与仿真. 系统仿真学报, 2009, 21(15): 4892-4895.
5.	张立藩, 张荣. 呼吸力学系统的模型与辨识. 航天医学与医学工程, 1990, 3(1): 58-65.
6.	丰继华, 李文娟, 陈建华, 等. 集总参数呼吸机模型仿真研究. 系统仿真学报, 2006, 18(10): 2959-2962.
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9.	Golden J F. Clark J W Jr, Stevens P M. Mathematical modeling of pulmonary airway dynamics. IEEE Trans Biomed Eng, 1973, 20(6): 397-404.
10.	Avanzolini G, Barbini P, Bernardi F, et al. Role of the mechanical properties of tracheobronchial airways in determining the respiratory resistance time course. Ann Biomed Eng, 2001, 29(7): 575-586.
11.	Barbini P, Cevenini G, Avanzolini G. Nonlinear mechanisms determining expiratory flow limitation in mechanical ventilation: a model-based interpretation. Ann Biomed Eng, 2003, 31(8): 908-916.
12.	Olender M F. Clark J W Jr, stevens P M.analog computer simulation of maximum expiratory flow limitation. IEEE Trans Biomed Eng, 1976, 23(6): 445-452.
13.	Liu C H, Niranjan S C, Clark J W, et al. Airway mechanics, gas exchange, and blood flow in a nonlinear model of the normal human lung. J Appl Physiol, 1998, 84(4): 1447-1469.
14.	Maarsingh E W, Van Eykern L A, Sprikkelman A B, et al. Respiratory muscle activity measured with a noninvasive EMG technique: technical aspects and reproducibility. J Appl Physiol, 2000, 88(6): 1955-1961.
15.	Athanasiades A, Ghorbel F, Clark J W, et al. Energy analysis of a nonlinear model of the normal human lung. Journal of Biological Systems, 2000, 8(2): 115-139.
16.	Mead J. Mechanical properties of lungs. Physiol Rev, 1961, 41(2): 281-330.
17.	范少光, 汤浩, 潘伟丰. 人体生理学, 译. 第 2 版. 北京: 北京医科大学出版社, 2000: 164.
18.	Kulish V. Human respiration: anatomy and physiology, mathematical modeling, numerical simulation and application. UK: WIT Press. 2006: 97.
19.	Jodat R W, Horgan J D, Lange R L. Simulation of respiratory mechanics. Biophys J, 1966, 6(6): 773-785.
20.	Hamid Q, Shannon J, Martin J. Physiologic basis of respiratory disease. Canada: BC Decker Inc, 2005: 55-60.
21.	慢性阻塞性肺疾病诊治指南(2013 年修订版). 中国医学前沿杂志(电子版), 2014, 6(2): 67-80.
22.	Pelosi P, Goldner M, Mckibben A, et al. Recruitment and derecruitment during acute respiratory failure: an experimental study. American Journal of Respiratory and Critical Care Medicine, 2001, 164(1): 122-130.
23.	Pecchiari M, Loring S H, D'angelo E. Esophageal pressure as an estimate of average pleural pressure with lung or chest distortion in rats. Respir Physiol Neurobiol, 2013, 186(2): 229-235.
24.	Hoppin F G, Green I D, Mead J, et al. Distribution of pleural surface pressure in dogs. J Appl Physiol, 1969, 27(6): 863-873.
25.	霍波, 付瑞荣, 梁晨, 等. 建立基于个体生理监护信息的呼吸力学模型. 医用生物力学, 2012, 27(4): 409-415.
26.	Tsai N C, Lee R M. Interaction between cardiovascular system and respiration. Appl Math Model, 2011, 35(11): 5460-5469.
27.	Chase J G, Preiser J C, Dickson J L, et al. Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomed Eng Online, 2018, 17: 24.

1. Weibel E R. Morphometry of the human lung. German: Springer-Verlag Berlin Heidelberg Gmbh, 1963: 110-135.
2. Ren Shuai, Cai Maolin, Shi Yan, et al. Influence of bronchial diameter change on the airflow dynamics based on a pressure-controlled ventilation system. Int J Numer Method Biomed Eng, 2018, 34(3): e2929.
3. Crooke P S, Hotchkiss J R, Marini J J. Linear and nonlinear mathematical models for noninvasive ventilation. Math Comput Model, 2002, 35(11/12): 1297-1313.
4. 易韦韦, 张玘, 王跃科, 等. 基于呼吸机的人工呼吸系统力学建模与仿真. 系统仿真学报, 2009, 21(15): 4892-4895.
5. 张立藩, 张荣. 呼吸力学系统的模型与辨识. 航天医学与医学工程, 1990, 3(1): 58-65.
6. 丰继华, 李文娟, 陈建华, 等. 集总参数呼吸机模型仿真研究. 系统仿真学报, 2006, 18(10): 2959-2962.
7. Huo B, Fu R R. Recent advances in theoretical models of respiratory mechanics. Acta Mechanica Sinica, 2012, 28(1): 1-7.
8. Polak A G, Mroczka J. Nonlinear model for mechanical ventilation of human lungs. Comput Biol Med, 2006, 36(1): 41-58.
9. Golden J F. Clark J W Jr, Stevens P M. Mathematical modeling of pulmonary airway dynamics. IEEE Trans Biomed Eng, 1973, 20(6): 397-404.
10. Avanzolini G, Barbini P, Bernardi F, et al. Role of the mechanical properties of tracheobronchial airways in determining the respiratory resistance time course. Ann Biomed Eng, 2001, 29(7): 575-586.
11. Barbini P, Cevenini G, Avanzolini G. Nonlinear mechanisms determining expiratory flow limitation in mechanical ventilation: a model-based interpretation. Ann Biomed Eng, 2003, 31(8): 908-916.
12. Olender M F. Clark J W Jr, stevens P M.analog computer simulation of maximum expiratory flow limitation. IEEE Trans Biomed Eng, 1976, 23(6): 445-452.
13. Liu C H, Niranjan S C, Clark J W, et al. Airway mechanics, gas exchange, and blood flow in a nonlinear model of the normal human lung. J Appl Physiol, 1998, 84(4): 1447-1469.
14. Maarsingh E W, Van Eykern L A, Sprikkelman A B, et al. Respiratory muscle activity measured with a noninvasive EMG technique: technical aspects and reproducibility. J Appl Physiol, 2000, 88(6): 1955-1961.
15. Athanasiades A, Ghorbel F, Clark J W, et al. Energy analysis of a nonlinear model of the normal human lung. Journal of Biological Systems, 2000, 8(2): 115-139.
16. Mead J. Mechanical properties of lungs. Physiol Rev, 1961, 41(2): 281-330.
17. 范少光, 汤浩, 潘伟丰. 人体生理学, 译. 第 2 版. 北京: 北京医科大学出版社, 2000: 164.
18. Kulish V. Human respiration: anatomy and physiology, mathematical modeling, numerical simulation and application. UK: WIT Press. 2006: 97.
19. Jodat R W, Horgan J D, Lange R L. Simulation of respiratory mechanics. Biophys J, 1966, 6(6): 773-785.
20. Hamid Q, Shannon J, Martin J. Physiologic basis of respiratory disease. Canada: BC Decker Inc, 2005: 55-60.
21. 慢性阻塞性肺疾病诊治指南(2013 年修订版). 中国医学前沿杂志(电子版), 2014, 6(2): 67-80.
22. Pelosi P, Goldner M, Mckibben A, et al. Recruitment and derecruitment during acute respiratory failure: an experimental study. American Journal of Respiratory and Critical Care Medicine, 2001, 164(1): 122-130.
23. Pecchiari M, Loring S H, D'angelo E. Esophageal pressure as an estimate of average pleural pressure with lung or chest distortion in rats. Respir Physiol Neurobiol, 2013, 186(2): 229-235.
24. Hoppin F G, Green I D, Mead J, et al. Distribution of pleural surface pressure in dogs. J Appl Physiol, 1969, 27(6): 863-873.
25. 霍波, 付瑞荣, 梁晨, 等. 建立基于个体生理监护信息的呼吸力学模型. 医用生物力学, 2012, 27(4): 409-415.
26. Tsai N C, Lee R M. Interaction between cardiovascular system and respiration. Appl Math Model, 2011, 35(11): 5460-5469.
27. Chase J G, Preiser J C, Dickson J L, et al. Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomed Eng Online, 2018, 17: 24.

Journal of Biomedical Engineering

Simulation of spontaneous breathing for healthy adults using a nonlinear airway-segmented model of respiratory mechanics

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