JBiSE  Vol.8 No.10 , October 2015
Modelling and Simulation of Pressure Controlled Mechanical Ventilation System
Author(s) Noman Q. Al-Naggar*
ABSTRACT
A mathematical model of mechanical ventilator describes its behavior during artificial ventilation. This paper purposes to create and simulate Mathematical Model (MM) of Pressure Controlled Ventilator (PCV) signal. This MM represents the respiratory activities and an important controlled parameter during mechanical ventilation—Positive End Expiration Pressure (PEEP). The MM is expressed and modelled using periodic functions with inequalities to control the beginning of inspiration and expiration durations. The created MM of PCV signal is combined with an existing multi compartmental model of respiratory system that is modified and developed in the internal parameters—compliances (C) to test created MM. The created MM and model of respiratory system are constructed and simulated using Simulink package in MATLAB platform. The obtained simulator of mechnical ventilation system could potentially represent the pressure signal of PVC as a complete respiratory cycle and continuance waveform. This simulator is also able to reflect a respiratory mechanic by changing some input variables such as inspiration pressure (IP), PEEP and C, which are monitored in volume, flow, pressure and PV loop waveforms. The obtained simulator has provided a simple environment for testing and monitoring PCV signal and other parameters (volume, flow and dynamic compliance) during artificial ventilation. Furthermore, the simulator may be used for studying in the laboratory and training ventilator’s operators.

Cite this paper
Al-Naggar, N. (2015) Modelling and Simulation of Pressure Controlled Mechanical Ventilation System. Journal of Biomedical Science and Engineering, 8, 707-716. doi: 10.4236/jbise.2015.810068.
References
[1]   Crooke, P.S., Marini, J.J. and Hotchkiss, J.R. (2002) Modeling Recruitment Maneuvers with a Variable Compliance Model for Pressure Controlled Ventilation. Journal of Theoretical Medicine, 4, 197-207.
http://dx.doi.org/10.1080/1027366021000023124

[2]   Hess, D.R. (2005) Ventilator Waveforms and the Physiology of Pressure Support Ventilation. 34th Respiratory Care Journal Conference, 50, 166-183.

[3]   Shi, Y., Ren, S., Cai, M.L., Xu, W.Q. and Deng, Q.Y. (2014) Pressure Dynamic Characteristics of Pressure Controlled Ventilation System of a Lung Simulator. Computational and Mathematical Methods in Medicine Online, Hindawi, 1-10.
http://dx.doi.org/10.1155/2014/761712

[4]   Steimle, K.L., Mogensen, M.L., Karbing, D.S., Bernardino de la Serna, J. and Andreassen, S. (2011) A Model of Ventilation of the Healthy Human Lung. Computer Methods and Programs in Bio-medicine, 101, 144-155.
http://dx.doi.org/10.1016/j.cmpb.2010.06.017

[5]   Khoo, M.C.K. (2001) Physiological Control Systems: Analysis, Simulation, and Estimation. IEEE Press Series on Biomedical Engineering, New York, 1-319.

[6]   Lakshmi, K.V. and Srinivas, P. (2012) Modeling, Simulation and Analysis of Lung Mechanics Using Labview. IJERT, 1, 1-8.

[7]   Lin, S.-L., Guo, N.-R. and Chiu, C.-C. (2010) Modeling and Simulation of Respiratory Control with Labview. Journal of Medical and Biological Engineering, 32, 51-60.
http://dx.doi.org/10.5405/jmbe.829

[8]   Chiew, Y.S., Chase, J.G., Shaw, G., Sundaresan, A. and Desaive, T. (2011) Model-Based PEEP Optimization in Mechanical Ventilation. Biomedical Engineering Online, 10, 111.
http://www.biomedical-engineering-online.com/content/10/1/111
http://dx.doi.org/10.1186/1475-925X-10-111


[9]   Sargent, R.G. (2013) Verification and Validation of Simulation Models. Journal of Simulation, 7, 12-24.
http://dx.doi.org/10.1057/jos.2012.20

[10]   Rittner, F. and Doring, M. (2005) Curves and Loops in Mechanical Ventilation. Drager Medical AG & Co. KG, Germany, 1-59.

[11]   Ahluwalia, J., Morley, C. and Wahle, H.G. (2014) Volume Guarantee New Approaches in Volume Controlled Ventilation for Neonates. DRAGER Corporation, Germany, 1-65.

[12]   Hernandez, A.M., Maanas, M.A. and Costa-Castelló, R. (2008) Learning Respiratory System Function in BME Studies by Means of a Virtual Laboratory: RespiLab. IEEE Transactions on Education, 51, 24-34.
http://dx.doi.org/10.1109/TE.2007.893355

[13]   Grossbach, I., Chlan, L. and Tracy, M.F. (2011) Overview of Mechanical Ventilatory Support and Management of Patient- and Ventilator-Related Responses. Critical Care Nurse, 31, 30-44.
http://dx.doi.org/10.4037/ccn2011595

[14]   Hess, D.R. (2005) Ventilator Waveforms and the Physiology of Pressure Support Ventilation. Respiratory Care, 50, 166-186.

[15]   Lucangelo, U., Bernabé, F. and Blanch, L. (2005) Respiratory Mechanics Derived from Signals in the Ventilator Circuit. Respiratory Care, 50, 55-67.

[16]   Tidal, L. (2000) Ventilation with Lower Tidal Volumes as Compared with Traditional Tidal Volumes for Acute Lung Injury and the Acute Respiratory Distress Syndrome. New England Journal of Medicine, 342, 1301-1308.
http://dx.doi.org/10.1056/NEJM200005043421801

[17]   Sundaresan, A., Yuta, T., Hann, C.E., Chase, J.G. and Shaw, G.M. (2009) A Minimal Model of Lung Mechanics and Model-Based Markers for Optimizing Ventilator Treatment in ARDS Patients. Computer Methods and Programs in Biomedicine, 95, 166-180.
http://dx.doi.org/10.1016/j.cmpb.2009.02.008

[18]   Britos, M., Smoot, E., Liu, K.D., Thompson, B.T., Checkley, W. and Brower, R.G., National Institutes of Health, Acute Respiratory Distress Syndrome Network (ARDS Network) Investigators (2011) The Value of PEEP and FiO2 Criteria in the Definition the Acute Respiratory Distress Syndrome. Critical Care Medicine, 39, 2025-2030.
http://dx.doi.org/10.1097/CCM.0b013e31821cb774

 
 
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