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

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.	易韋韋, 張玘, 王躍科, 等. 基于呼吸機的人工呼吸系統力學建模與仿真. 系統仿真學報, 2009, 21(15): 4892-4895.
5.	張立藩, 張榮. 呼吸力學系統的模型與辨識. 航天醫學與醫學工程, 1990, 3(1): 58-65.
6.	豐繼華, 李文娟, 陳建華, 等. 集總參數呼吸機模型仿真研究. 系統仿真學報, 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, 湯浩, 潘偉豐. 人體生理學, 譯. 第 2 版. 北京: 北京醫科大學出版社, 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.	慢性阻塞性肺疾病診治指南(2013 年修訂版). 中國醫學前沿雜志(電子版), 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, 付瑞榮, 梁晨, 等. 建立基于個體生理監護信息的呼吸力學模型. 醫用生物力學, 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. 易韋韋, 張玘, 王躍科, 等. 基于呼吸機的人工呼吸系統力學建模與仿真. 系統仿真學報, 2009, 21(15): 4892-4895.
5. 張立藩, 張榮. 呼吸力學系統的模型與辨識. 航天醫學與醫學工程, 1990, 3(1): 58-65.
6. 豐繼華, 李文娟, 陳建華, 等. 集總參數呼吸機模型仿真研究. 系統仿真學報, 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, 湯浩, 潘偉豐. 人體生理學, 譯. 第 2 版. 北京: 北京醫科大學出版社, 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. 慢性阻塞性肺疾病診治指南(2013 年修訂版). 中國醫學前沿雜志(電子版), 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, 付瑞榮, 梁晨, 等. 建立基于個體生理監護信息的呼吸力學模型. 醫用生物力學, 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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