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Evolution of blood pressure control identification in lieu of post-surgery diabetic patients: a review

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Abstract

The blood pressure disparity is the major problem in post-operative surgery especially diabetic patients, because there is substantial interrelation between diabetic and hypertension and this abnormality creates complicated problems and needs to be controlled by continuous monitoring based on the severity. To overcome this problem, implementation of automatic drug infusion is required for critical patients, by which workload of the clinical staffs are reduced. Most commonly the sodium nitroprusside (SNP) is used to reduce the blood pressure in fast action based on the prescribed level. In this paper three different types of estimation techniques (PID, IMC and MPC) are uses to identify the valuation. The strength of the projected controller performance is evaluated under different types of patients such as sensitive, and normal along with insensitive patients. Therefore, this paper review the validation results based on the optimized SNP infusion rate for persistent Blood pressure control compare then the reviewed methods. The MATLAB simulation is used to evaluate the efficiency of the proposed work and obtain the results based on the projected values.

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References

  1. Slate JB, Sheppard LC, Rideout VC, et al. Closed-loop ni- troprusside infusion: modeling and control theory for clinical application. In: Proceedings of IEEE international symposium on circuits systems. 1980;482–8.

  2. Cheung BM, Li C. Diabetes and hypertension: is there a common pathway. The current atherosclerosis reports, vol 14, Springer. 2012;160–6.

  3. Mugo MN, Stump CS, Rao PG, et al. Hypertension and diabetes mellitus. In: Black HR, Elliott WJ, editors. Hypertension: a companion to Braunwald’s heart disease. Elsevier. 2007; p. 409.

  4. Basha Alavudeen, Vivekanandan S. Evolution of diabetic control identification in lieu of continuous glucose monitoring technology—a review. Int J Appl Eng Res. 2017;12(16):6102–7.

    Google Scholar 

  5. Behbehani K, Cross RR. A controller for regulation of mean arterial blood pressure using optimum nitroprusside infusion rate. IEEE Trans Bio-med Eng. 1991;38:513–21.

    Article  Google Scholar 

  6. Frei C, Derighetti M, Morari M, Glattfelder A, Zbinden A. Improved regulation of mean arterial blood pressure during anesthesia through estimates of surgery effects. IEEE Trans Bio- med Eng. 2000;47:1456–64.

    Article  Google Scholar 

  7. Bajzer Ž, Marušic M, Vuk-Pavlovic S. Conceptual frameworks for mathematical modelling of tumor growth dynamics. Math Comput Model. 1996;23:31–46.

    Article  Google Scholar 

  8. Bergman R, Phillips L, Cobelli C. Physiologic evaluation of factors controlling glucose tolerance in man. J Clin Investig. 1981;68:1456–67.

    Article  Google Scholar 

  9. Abdel M, Manogaran G, Rashad H, Zaied ANH. A comprehensive review of quadratic assignment problem: variants, hybrids and applications. J Ambient Intell Hum Comput. 2018. https://doi.org/10.1007/s12652-018-0917-x.

    Article  Google Scholar 

  10. Abdel-Basset M, Manogararan G, Chilamkurti N. Three-way decisions based on neutrosophic sets and AHP-QFD framework for supplier selection problem. Future Gen Comput Syst. 2018. https://doi.org/10.1016/j.future.2018.06.024.

    Article  Google Scholar 

  11. Jeffrey AM, Xiaohua X, Craig IK. When to initiate HIV therapy: a control theoretic approach. IEEE Trans Bio-med Eng. 2003;50:1213–20.

    Article  Google Scholar 

  12. Slate JB, Sheppard LC. Automatic control of blood pres- sure by drug infusion. IEE Proc Part A. 1982;9:639–45.

    Google Scholar 

  13. Hernandez L, Shankar R, Pajunen G. A microprocessor based drug infusion control system employing a model reference adaptive control algorithm to regulate blood pressure in I.C.U. patients. In: Proceedings of the IEEE Southeastcon. 1989;1261–6.

  14. Reves JG, Sheppard LC, Wallach R, Lell WA. Therapeutic uses of Sodium Nitroprusside and an automated method of administration. Int Anesthesiol Clin. 1978;16:51–88.

    Article  Google Scholar 

  15. Abdel-Basset M, El-Shahat D, Mirjalili S. A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem. Future Gen Comput Syst. 2018;85:129–45.

    Article  Google Scholar 

  16. Abdel-Basset M, Manogaran G, Abdel-Fatah L, Mirjalili S. An improved nature inspired meta-heuristic algorithm for 1-D bin packing problems. Pers Ubiquitous Comput. 2018. https://doi.org/10.1007/s00779-018-1132-7.

    Article  Google Scholar 

  17. Abdel-Basset M, Manogaran G, Gamal A, Smarandache F. A hybrid approach of neutrosophic sets and DEMATEL method for developing supplier selection criteria. Des Autom Embed Syst. 2018. https://doi.org/10.1007/s10617-018-9203-6.

    Article  Google Scholar 

  18. Kovio AJ, Smollen VF, Barile RV. An automated drug administration system to control blood pressure in rabbits. Math Biosci. 1978;38:45–56.

    Article  Google Scholar 

  19. Sheppard LC, Shotts JF, Robertson NF, Wallace FD, Kouchoukos NT. Computer controlled infusion of vasoactive drugs in post cardiac surgical patients. In: Conference proceedings IEEE engineering in medicine and biology society. 1979;280–4.

  20. Slate JB, Sheppard LC, Rideout VC, Blackstone EH. A model for design of a blood pressure controller for hypertensive patients. In: Proceedings of the IEEE EMBS Conference. 1979;867–72.

    Article  Google Scholar 

  21. Slate JB, Sheppard LC. A model-based adaptive blood pressure controller. In: Proceedings of IFAC symposium on identification and system parameter estimation, Washington, DC. 1982;1982:1437–42.

  22. Martin JF, Schneider AM, Smith NT. Multiple-model adaptive control of blood pressure using sodium nitroprusside. IEEE Trans Bio-med Eng. 1987;34:603–11.

    Article  Google Scholar 

  23. Abdel-Basset M, Gunasekaran M, Mohamed M, Smarandache F. A novel method for solving the fully neutrosophic linear programming problems. Neural Comput Appl. 2018. https://doi.org/10.1007/s00521-018-3404-6.

    Article  Google Scholar 

  24. Abdel-Basset M, Manogaran G, Fakhry AE, El-Henawy I. 2-Levels of clustering strategy to detect and locate copy-move forgery in digital images. Multimed Tools Appl. 2018. https://doi.org/10.1007/s11042-018-6266-0.

    Article  Google Scholar 

  25. Abdel-Basset M, Manogaran G, Mohamed M. Internet of things (IoT) and its impact on supply chain: a framework for building smart, secure and efficient systems. Future Gen Comput Syst. 2018. https://doi.org/10.1016/j.future.2018.04.051.

    Article  Google Scholar 

  26. Basha AA, Vivekanandan S. Optimal control identification of IMC and PID controllers for insulin infusion. In: CTCCEC IEEE conference 2017. Astrom KJ, Hagglund T, “PID controllers: theory, de- sign and tuning. Research Triangle Park, NC: ISA, 1995.

  27. Kaufman H, Roy R, Xu X. Model reference adaptive control of drug infusion rate. Automatica. 1984;20:205–9.

    Article  Google Scholar 

  28. Arnsparger JM. Adaptive control of blood pressure. IEEE Trans Bio-med Eng. 1983;30:168–76.

    Article  Google Scholar 

  29. Hahn J, Edison T, Edgar TF. Adaptive IMC control for drug infusion for biological systems. Control Eng Pract. 2002;10:45–56.

    Article  Google Scholar 

  30. Enbiya E, Hossain E, Mahieddine F. Performance of optimal IMC and PID controllers for blood pressure control. IFMBE Proc. 2009;24:89–94.

    Article  Google Scholar 

  31. Basset M, Manogaran G, Mohamed M, Rushdy E. Internet of things in smart education environment: supportive framework in the decision-making process. Concurr Comput: Pract Exp. 2018. https://doi.org/10.1002/cpe.4515.

    Article  Google Scholar 

  32. Bequette BW. Process control: modeling, design, and simulation. Upper Saddle River, New Jersey: Prentice-Hall Inc; 2003.

    Google Scholar 

  33. Brosilow C, Joseph B. Techniques of model-based control. Englewood Cliffs, NJ: Prentice-Hall; 2002.

    Google Scholar 

  34. Zhao Z, Liu Z, Zhang J. IMC-PID tuning method based on sensitivity specification for process with time-delay. J Cent S Univ Technol. 2011;18:1153–60.

    Article  Google Scholar 

  35. Hu W, Xiao G, Cai W. PID controller design based on two- degrees-of-freedom direct synthesis. In: Chinese control decision conference. 2011;629–34.

  36. Parker RS, Doyle FJ. Control-relevant modelling in drug delivery. Adv Drug Deliv Rev. 2001;48:211–28.

    Article  Google Scholar 

  37. Shook DS, Mohtadi C, Shah SL. A control-relevant identification strategy for GPC. IEEE Trans Automat Control. 1992;37:975–80.

    Article  Google Scholar 

  38. Mäkilä P, Partington JR, Gustafsson TK. Worst-case control- relevant identification. Automatica. 1995;31:1799–819.

    Article  MathSciNet  Google Scholar 

  39. Slate JB. Model-based design of a controller for infusion sodium nitroprusside during postsurgical hypertension. Ph.D. Thesis, Univ. Wisconsin-Madison. 1980.

  40. Slate JB, Sheppard LC, Rideout VC, Blackstone EH. Closed- loop nitroprusside infusion: modelling and control theory for clinical application. In: Proceedings of the IEEE international symposium on circuits and system. 1980;482–8.

  41. Parthasarathy P, Vivekanandan S. A comprehensive review on thin film-based nano-biosensor for uric acid determination: arthritis diagnosis. World Rev Sci Technol Sustain Dev. 2018;14(1):52–71.

    Article  Google Scholar 

  42. Parthasarathy P, Vivekanandan S. A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases. Inform Med Unlocked. 2018. https://doi.org/10.1016/j.imu.2018.03.001.

    Article  Google Scholar 

  43. Saxena S, Hote YV. Advances in internal model control technique: a review and future prospects. IETE Tech Rev. 2012;29(6):461–72.

    Article  Google Scholar 

  44. Isaka S, Sebald AV. Control strategies for arterial blood pressure regulation. IEEE Trans Biomed Eng. 1993;40:353–63.

    Article  Google Scholar 

  45. MATLAB SIMULINK, Simulink® Response Optimization™ 3 User’s Guide. The MathWorks, Inc. 2004–2008.

  46. Tanaka JLK, Wakasa Y, Mizukami Y. GA type IMC control pneumatic servo system. Proceedings of SICE annual conference, Sapporo. 2004;1:791–794.

  47. Parthasarathy P, Vivekanandan S. Investigation on uric acid biosensor model for enzyme layer thickness for the application of arthritis disease diagnosis. Health Inf Sci Syst. 2018;6:1–6.

    Google Scholar 

  48. Parthasarathy P, Vivekanandan S. A typical IoT architecture-based regular monitoring of arthritis disease using time wrapping algorithm. Int J Comput Appl. 2018. https://doi.org/10.1080/1206212X.2018.1457471.

    Article  Google Scholar 

  49. Rivals I, Personnaz L. Nonlinear internal model control using neural networks: application to processes with delay and design issues. IEEE Trans Neural Netw. 2000;11(1):80–90.

    Article  Google Scholar 

  50. Garcia CE, Morari MA. Internal model control. Unifying review and some new results. Ind Eng Chem Process Des Dev. 1982;21(2):308–23.

    Article  Google Scholar 

  51. Poterlowicz K, Hossain MA, Majumder MAA. Performances of optimisation algorithms for IMC based blood pressure control. In: International conference on software, knowledge, information management and applications, SKIMA. 2008;1–6.

  52. Astrom KJ, Hagglund T, Hang CC, et al. Automatic tuning and adaptation for PID controllers—a survey. IFACJ Control Eng Pract. 1993;1(4):699–714.

    Article  Google Scholar 

  53. Auer LM, Rodler H. Microprocessor- control of drug in- fusion for automatic blood-pressure control. Med Biol Eng Comput. 1981;19:171–4.

    Article  Google Scholar 

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Basha, A.A., Vivekanandan, S. & Parthasarathy, P. Evolution of blood pressure control identification in lieu of post-surgery diabetic patients: a review. Health Inf Sci Syst 6, 17 (2018). https://doi.org/10.1007/s13755-018-0055-z

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