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Exploratory mathematical frameworks and design of control systems for the automation of propofol anesthesia

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Abstract

A variety of automatic control systems are increasingly being deployed to assist clinicians to monitor patient functioning and enhance healthcare delivery during surgical procedures. This article deals with the mathematical design framework of closed-loop infusion schemes for propofol delivery in general anesthesia. The main emphasis of this research series is to come up with a better-performing control system which could handle the clinical concerns of automation-based anesthesia without compromise of safety. Also, the research is geared at studying the performance of these plausible control-based automatic drug infusion patterns in a computer environment prior to actual clinical implementation. The study advances the design of effective model-based predictive control (MPC) strategies familiar to engineers in the process industries, as well as a preliminary design of a proportional–integral–derivative (PID) controller. The consideration of the traditional PID controller is followed by two linear MPC strategies and a nonlinear one. These varieties of closed-loop infusion strategies were designed in order to make well-informed comparison and assessment of the promising method(s) of control for the sought clinical application. The successive linearization technique is being applied in novelty to anesthesia in this work. The results indicate that the MPC controllers show great promise for adoption for automated drug delivery in anesthesia delivering better performance. This sets the pace for future investigations which may assess, via pseudo-clinical in silico studies, the deployment of the controllers.

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reproduced from The MathWorks, 2004 [19]

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References

  1. Bequette BW (2003) Process control: modeling, design and simulation. Prentice Hall, New Jersey, USA

    Google Scholar 

  2. Nascu, I. and Pistikopoulos, E. N. (2016), Multiparametric model predictive control strategies of the hypnotic component in intravenous anesthesia. In: proceedings of the IEEE international conference on systems, man and cybernetics, October 9–12, 2016, Budapest, Hungary, pp. 2828–2833

  3. Nascu I, Krieger A, Ionescu CM, Pistikopoulos EN (2015) Advanced model-based control studies for the induction and maintenance of intravenous anaesthesia. IEEE Trans Biomed Eng 62(3):832–841

    Article  Google Scholar 

  4. Krieger A, Pistikopoulos EN (2014) Model predictive control of anesthesia under uncertainty. Comput Chem Eng 71:699–707

    Article  Google Scholar 

  5. Niño J, De Keyser R, Syafiie S, Ionescu C, Struys M (2009) EPSAC-controlled anesthesia with online gain adaptation. Int J Adapt Control Signal Process 23(5):455–471

    Article  MATH  Google Scholar 

  6. Yelneedi S, Samavedham L, Rangaiah GP (2009) Advanced control strategies for the regulation of hypnosis with propofol. Ind Eng Chem Res 48(8):3880–3897

    Article  Google Scholar 

  7. Syafiie S, Niño J, Ionescu C, De Keyser R (2009) NMPC for propofol drug dosing during anesthesia induction. In: Magni L, Raimondo DM, Allgöwer F (eds) Nonlinear model predictive control: towards new challenging applications, vol 384. Lecture notes in control and information sciences. Berlin, Heidelberg, pp 501–509

    Chapter  Google Scholar 

  8. Sawaguchi Y, Furutani E, Shirakami G, Araki M, Fukuda K (2008) A model-predictive hypnosis control system under total intravenous anesthesia. IEEE Trans Biomed Eng 55(3):874–887

    Article  Google Scholar 

  9. Araki M, Furutani E (2005) Computer control of physiological states of patients under and after surgical operation. Annu Rev Control 29(2):229–236

    Article  Google Scholar 

  10. Ionescu CM, Keyser RD, Torrico BC, Smet TD, Struys MMRF, Normey-Rico JE (2008) Robust predictive control strategy applied for propofol dosing using BIS as a controlled variable during anesthesia. IEEE Trans Biomed Eng 55(9):2161–2170

    Article  Google Scholar 

  11. Nunes, C. S., Mendonça, T., Lemos, J. M. and Amorim, P. (2007), Control of depth of anesthesia using MUSMAR - exploring electromyography and the analgesic dose as accessible disturbances. In: proceedings of the 29th annual international conference of the IEEE engineering in medicine and biology society, August 23–26, 2007, Lyon, France, pp. 1574–1577

  12. Rao RR, Bequette BW, Roy RJ (2000) Simultaneous regulation of hemodynamic and anesthetic states: a simulation study. Ann Biomed Eng 28(1):71–84

    Article  Google Scholar 

  13. Nunes, C. S., Mendonça, T., Lemost, J. M. and Amorim, P. (2007), Predictive adaptive control of the bispectral index of the EEG (BIS): Exploring electromyography as an accessible disturbance. In: 2007 Mediterranean conference on control and automation, July 27–29, 2007, Athens, Greece

  14. Eskandari N, van Heusden K, Dumont GA (2020) Extended habituating model predictive control of propofol and remifentanil anesthesia. Biomed Signal Process Control 55:101656

    Article  Google Scholar 

  15. Savoca A, Manca D (2019) A physiologically-based approach to model-predictive control of anesthesia and analgesia. Biomed Signal Process Control 53:101553

    Article  Google Scholar 

  16. Ntouskas S, Sarimveis H (2021) A robust model predictive control framework for the regulation of anesthesia process with Propofol. Optim Control Appl Methods 42(4):965–986

    Article  MathSciNet  MATH  Google Scholar 

  17. Copot, D., Kusse, F., Ghita, M., Ghita, M., Neckebroek, M., & Maxim, A. (2019). Distributed model predictive control for hypnosis-hemodynamic maintenance during anesthesia. In: 2019 23rd international conference on system theory, control and computing (ICSTCC) (pp. 638–643). IEEE

  18. Maxim A, Copot D (2021) Closed-loop control of anesthesia and hemodynamic system: a model predictive control approach. IFAC-PapersOnLine 54(15):37–42

    Article  Google Scholar 

  19. Adhau, S., Patil, S., Ingole, D., & Sonawane, D. (2019). Embedded implementation of deep learning-based linear model predictive control. In: 2019 sixth Indian control conference (ICC) (pp. 200–205). IEEE

  20. Copot, D., & Maxim, A. (2019). Model predictive control for simultaneous regulation of hypnosis and hemodynamic states. In: 2019 18th European control conference (ECC) (pp. 4106–4111). IEEE

  21. Jing CJ, Syafiie S (2021) Multi-model generalised predictive control for intravenous anaesthesia under inter-individual variability. J Clin Monit Comput 35(5):1037–1045

    Article  Google Scholar 

  22. Savoca A, Barazzetta J, Pesenti G, Manca D (2018) Model predictive control for automated anesthesia. Comput Aid Chem Eng 43:1631–1636

    Article  Google Scholar 

  23. Hosseinzadeh M, van Heusden K, Yousefi M, Dumont GA, Garone E (2020) Safety enforcement in closed-loop anesthesia—a comparison study. Control Eng Practice 105:104653

    Article  Google Scholar 

  24. Bequette BW, Doyle FJ (2001) Automated control in biomedicine. IEEE Eng Med Biol Mag 20(1):22–23

    Article  Google Scholar 

  25. Bickford RG (1950) Automatic electroencephalographic control of general anesthesia. Electroencephalogr Clin Neurophysiol 2(1–4):93–96

    Article  Google Scholar 

  26. Beck C, Lin H-H, Bloom M (2007) Modeling and control of anesthetic pharmacodynamics. In: Queinnec I, Tarbouriech S, Garcia G, Niculescu S-I (eds) Biology and control theory: current challenges, vol 357. Lecture notes in control and information sciences. Berlin, Heidelberg, pp 263–289

    Chapter  Google Scholar 

  27. Marsh B, White M, Morton N, Kenny GNC (1991) Pharmacokinetic model driven infusion of propofol in children. Br J Anaesth 67(1):41–48

    Article  Google Scholar 

  28. Oshin TA (2016) Automation in anaesthesia. Lambert Academic Publishing, Saarbrucken, Germany

    Google Scholar 

  29. The MathWorks (2004), Model Predictive Control ToolboxTM User’s Guide, Version 2.0, The MathWorks Inc., USA

  30. Al Seyab RK, Cao Y (2006) Nonlinear model predictive control for the ALSTOM gasifier. J Process Control 16(8):795–808

    Article  Google Scholar 

  31. Rossiter JA (2003) Model-based predictive control: a practical approach. CRC Press, Boca Raton, Florida, USA

    Google Scholar 

  32. Rowe WL (1998) Economics and anaesthesia. Anaesthesia 53:782–788

    Article  Google Scholar 

  33. Sreenivas Y, Yeng TW, Rangaiah GP, Lakshminarayanan S (2009) A comprehensive evaluation of PID, cascade, model-predictive, and RTDA controllers for regulation of hypnosis. Ind Eng Chem Res 48(12):5719–5730

    Article  Google Scholar 

  34. Simanski O, Janda M, Schubert A, Bajorat J, Hofmockel R, Lampe B (2009) Progress of automatic drug delivery in anaesthesia-the “Rostock assistant system for anaesthesia control (RAN).” Int J Adapt Control Signal Process 23(5):504–521

    MATH  Google Scholar 

  35. Moore, B. L., Pyeatt, L. D. and Doufas, A. G. (2009), Fuzzy control for closed-loop, patient-specific hypnosis in intraoperative patients: a simulation study. In: proceedings of the 31st annual international conference of the IEEE engineering in medicine and biology society, September 2–6, 2009, Minneapolis, Minnesota, USA, pp. 3083–3086

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Acknowledgements

The guidance of Prof. Yi Cao is acknowledged during the conduct of the research at Cranfield University. Also, the financial sponsorship of the PTDF Nigeria provided to the author in the form of postgraduate study scholarship is acknowledged.

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Correspondence to Temitope A. Oshin.

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I hereby declare that this manuscript is the result of my independent creation under the reviewers’ comments. Except for the quoted contents, this manuscript does not contain any research achievements that have been published or written by other individuals or groups. I am the only author of this manuscript. The legal responsibility of this statement shall be borne by me.

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Oshin, T.A. Exploratory mathematical frameworks and design of control systems for the automation of propofol anesthesia. Int. J. Dynam. Control 10, 1858–1875 (2022). https://doi.org/10.1007/s40435-022-00953-1

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  • DOI: https://doi.org/10.1007/s40435-022-00953-1

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