• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2020, Volume: 13, Issue: 12, Pages: 1347-1354

Original Article

Modeling and optimized controller for cardiac drug infusion system

Received Date:31 March 2020, Accepted Date:23 April 2020, Published Date:08 May 2020


Background: Manual drug infusion during surgeries is inaccurate and timeconsuming which has been adopted technique in most of the hospitals. Methods: Based on clinical data, it is evident that the manual control is inaccurate and takes prolonged time to bring into effect, if any change in infusion rate is required during clinical practice. Considering this drawback, the modeling of cardiovascular system (CVS) and baroreceptor (BR) is developed using miscellaneous differential equations based on compartmental approach. The control variables are mean arterial pressure (MAP) and cardiac output (CO) obtained from CVS-BR model. Findings: The manipulated variables are noradrenaline (NAR) and nitroglycerine (NG) infusion rate which is modeled using the relationship between volume and drug mass effect equations on CVS-BR model. For the open loop transfer function derived from the model, relative gain array (RGA) analysis is performed to identify the influence of maximum effect of manipulated variables on the physiological parameters. The simulation results obtained from MATLAB are correlated using time domain specifications and error criteria. The performance index reveals the least error and facilitates in accurate infusion of drugs to the patient during cardiovascular surgeries. Novelty: The automatic controller during surgery provides safe operating condition and speedy recovery of the patients and also helps the anaesthetist to monitor and regulate the physiological variables.

Keywords: Control strategy; Error criteria; Drug infusion; Modeling; Relative gain array; Simulation


  1. Megumi N, Kazuma M, Ryo H, Ken-Ichi K, Koji K, Isao S, et al. Cardiovascular Modeling of Congenital Heart Disease Based on Neonatal Echocardiographic Images. IEEE Transactions on Information Technology in Biomedicine. 2012;16(1):70–79.
  2. Marques AG, Soguero-Ruiz C, Ramos J, Mora-Jimenez I, Goya-Esteban R, Garcia-Carretero R, et al. Modelling Cardiovascular Condition Evolution in Hypertensive Population Using Graph Signal Processing. Computing in Cardiology. 2017;44:1–4.
  3. Le TQ, Bukkapatnam STS, Komanduri R. Real-Time Lumped Parameter Modeling of Cardiovascular Dynamics Using Electrocardiogram Signals: Toward Virtual Cardiovascular Instruments. IEEE Transactions on Biomedical Engineering. 2013;60(8):2350–2360. doi: 10.1109/tbme.2013.2256423
  4. Bighamian R, Soleymani S, Reisner AT, Seri I, Hahn JO. Prediction of Hemodynamic Response to Epinephrine via Model-Based System Identification. IEEE Journal of Biomedical and Health Informatics. 2016;20(1):416–423. doi: 10.1109/jbhi.2014.2371533
  5. Davos CH, Davies LC, Piepoli M. The Effect of Baroreceptor Activity on Cardiovascular Regulation. Hellenic Journal of Cardiology. 2002;43:145–155.
  6. Albaghdadi M. 2007.
  7. Ottesen JT. Modelling the dynamical baroreflex-feedback control. Mathematical and Computer Modelling. 2000;31(4-5):167–173. doi: 10.1016/s0895-7177(00)00035-2
  8. Nirmala SA, Muthu R, Veena Abirami B. Model Predictive Control of Drug Infusion System for Mean Arterial Pressure Regulation of Critical Care Patients. Research Journal of Applied Sciences, Engineering and Technology. 2014;7(21):4601–4605. doi: 10.19026/rjaset.7.839
  9. Ridha M, TM. Model Predictive Control Blood Pressure by Drug Infusion. Communications, Control and Systems Engineering. 2011;11(1):32–45.
  10. Anju C, Nafeesa K. Control Scheme for Arterial Blood Pressure Regulation in Hypertensive Patients. International Journal of Advanced Information Science and Technology. 2014;30(30):517–520.
  11. Arpita B, Ashoke S. Online Identification and Internal Model Control for Regulating Hemodynamic Variables in Congestive Heart Failure Patient. International Journal of Pharma Medicine and Biological Sciences. 2015;24(2):85–89.
  12. Niu B, Wang H, Wang J, Tan L. Multi-objective bacterial foraging optimization. Neurocomputing. 2013;116:336–345. doi: 10.1016/j.neucom.2012.01.044
  13. Saber AY. Economic dispatch using particle swarm optimization with bacterial foraging effect. International Journal of Electrical Power & Energy Systems. 2012;34(1):38–46.
  14. Sowparnika G, Thirumarimurugan M, Sivakumar V, Vinoth N. Controlled infusion of intravenous cardiac drugs using global optimization. Indian Journal of Pharmacology. 2019;51(1):61. doi: 10.4103/ijp.ijp_612_18
  15. Karar ME, El-Brawany MA. Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller. Biomedical Engineering and Computational Biology. 2011;3:BECB.S6495. doi: 10.4137/becb.s6495
  16. Huang JW, Held CM, Roy RJ. Drug infusion for control of blood pressure during anesthesia. Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. 2000.
  17. UEMURA K, SUGIMACHI M. Automated Cardiovascular Drug Infusion System to Control Hemodynamics. Advanced Biomedical Engineering. 2013;2(0):32–37. doi: 10.14326/abe.2.32


Copyright: © 2020 Sowparnika, Thirumarimurugan, Vinoth. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)


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