Skip to main content

Advertisement

SpringerLink
Log in

A fuzzy-based adaptive multi-input–output scheme in lieu of diabetic and hypertension management for post-operative patients: an human–machine interface approach with its continuum

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Here, a multi-input–output control strategy-based adaptive system is implemented to regulate the glycaemic and hypertension (HT) level concurrently for post-operative patients. The current medical research identifies the relationship between diabetes and HT, and these two diseases have possible overlap in their disease aetiology. Based on the continuum, the blood glucose and blood pressure (BP) have to be measured independently and control the infusion to maintain optimally based on the variations of diabetic mellitus and HT to avoid the major complications for the perioperative condition. Based on the analysis, the post-operative strain may increase HT and may increase the glycaemic level, and this uncertainty of disparity may lead to BP, hyperinsulinemia, cardio diseases, and osmotic diuresis along with hyperglycaemia. The proposed adaptive cascade control strategy illustrates two different types of control loops independently, and these loops adopt an adaptive control strategy with parametric compensation. This adaptive control algorithm integrates the cascade methodology along with expert knowledge to treat these diseases by using adaptation laws with the help of fuzzy logic to regulate the proper insulin and sodium nitroprusside (SNP) infusion. The output response of the plasma glucose concentration and HT regulation was shown along with insulin infusion rate and SNP infusion rate based on the extensive simulation readings. The attained simulation results demonstrate that the adaptive control strategy shows better outcomes for the infusion and may achieve potentially better control on HT and glycaemic levels for post-operative patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Gurushankar G, Showers JR et al (2006) Hypertension and diabetes mellitus. In: European cardiovascular disease—Reference section, pp 1–5

  2. Wild S, Roglic G, Green A, Sicree R, King H (2004) Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27(5):1047–1053

    Article  Google Scholar 

  3. Slate JB, Sheppard LC, Rideout VC, Blackstone EH (1979) A model for design of a blood pressure controller for hypertensive patients. In: Proceedings of the IEEE EMBS conference, pp 867–72

  4. Parthasarathy P, Vivekanandan S (2018) A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases. Inf Med Unlocked 12:143–147

    Article  Google Scholar 

  5. Slate JB, Sheppard LC (1982) A model-based adaptive blood pressure controller. In: Proceedings of IFAC symposium on identification and system parameter estimation, Washington, pp 1437–1442

  6. Parthasarathy P, Vivekanandan S (2018) Investigation on uric acid biosensor model for enzyme layer thickness for the application of arthritis disease diagnosis. Health Inf Sci Syst 6(1):5

    Article  Google Scholar 

  7. Alavudeen Basha A, Vivekanandan S (2019) Enhanced optimal insulin regulation in post-operative diabetic patients: an adaptive cascade control compensation-based approach with diabetic and hypertension. IEEE Access 7:90973–90981

    Article  Google Scholar 

  8. Cheung BMY, Li C (2012) Diabetes and hypertension: is there a common metabolic pathway. Coron Heart Dis 14:160–166

    Google Scholar 

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

    Google Scholar 

  10. Basha AA, Vivekanandan S, Parthasarathy P (2018) Evolution of blood pressure control identification in lieu of post-surgery diabetic patients: a review. Health Inf Sci Syst 6(1):17

    Article  Google Scholar 

  11. ADA (2002) Standards of medical care for patients with diabetes mellitus. Diabetes Care 25(1):533–549

    Google Scholar 

  12. Landsberg L, Molitch M (2004) Diabetes and hypertension: pathogenesis, prevention and treatment. Clin Exp Hypertens 26:621–628

    Article  Google Scholar 

  13. Gress TW, Nieto FJ, Shahar E et al (2000) Hypertension and antihypertensive therapy as risk factors for type 2 diabetes mellitus. Atherosclerosis Risk in Communities Study. N Engl J Med 342:905–912

    Article  Google Scholar 

  14. Alavudeen Basha A, Vivekanandan S, Parthasarathy P (2019) Blood glucose regulation for post-operative patients with diabetics and hypertension continuum: a cascade control-based approach. J Med Syst 43(4):1–10

    Google Scholar 

  15. Zeggini E, Scott LJ, Saxena R et al (2008) Meta-analysis of genomewide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40:638–645

    Article  Google Scholar 

  16. Parthasarathy P, Vivekanandan S (2020) A typical IoT architecture-based regular monitoring of arthritis disease using time wrapping algorithm. Int J Comput Appl 42(3):222–232. https://doi.org/10.1080/1206212X.2018.1457471

    Article  Google Scholar 

  17. Sober S, Org E, Kepp K et al (2009) Targeting 160 candidate genes for blood pressure regulation with a genome-wide genotyping array. PLoS ONE 4:e6034

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Bequette BW (2003) Process control: modeling, design, and simulation. Prentice-Hall, Inc., Upper Saddle River

    Google Scholar 

  20. Alavudeen Basha S, Vivekanandan S (2017) Evolution of diabetic control identification in lieu of continuous glucose monitoring technology—a review. Int J Appl Eng Res 12(16):6102–6107

    Google Scholar 

  21. Meier R, Nieuwland J, Zbinden AM, Hacisalihzade SS (1992) Fuzzy logic control of blood pressure during anesthesia. IEEE Control Syst Mag 12(6):12–16

    Article  Google Scholar 

  22. Bergman RN, Ider YZ, Bowden C, Cobelli C (1979) Quantitative estimation of insulin sensitivity. Am Physiol Soc 236(6):E667–E677

    Google Scholar 

  23. Vijayarajeswari R, Parthasarathy P, Vivekanandan S, Alavudeen Basha A (2019) Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement 146:800–805

    Article  Google Scholar 

  24. Bergman RN, Urquhart J (1971) The pilot gland approach to the study of insulin secretory dynamics. Recent Prog Horm Res 27:583–605

    Google Scholar 

  25. Ganesh J, Viswanathan V (2011) Management of diabetic hypertensives. Indian J Endocrinol Metab 15(Supplement 4):374–379

    Google Scholar 

  26. Varon J, Marik PE (2008) Perioperative hypertension management. Vasc Health Risk Manag Hypertens 4(3):615–627

    Article  Google Scholar 

  27. Dagogo-Jack Samuel (2002) Management of diabetes mellitus in surgical patients. Diabetes Spectr 15(1):44–48

    Article  Google Scholar 

  28. Al-Niaimi AN, Hartenbach E (2015) Intensive postoperative glucose control reduces the surgical site infection rates in gynecologic oncology patients. Gynecol Control 136(1):71–76

    Google Scholar 

  29. Parthasarathy P, Vivekanandan S (2019) Biocompatible TiO2–CeO2 nano-composite synthesis, characterization and analysis on electrochemical performance for uric acid determination. Ain Shams Eng J. https://doi.org/10.1016/j.asej.2019.11.011

    Article  Google Scholar 

  30. Bailey JM, Haddad WM (2005) Drug dosing control in clinical pharmacology. IEEE Control Syst Mag 25(2):35–51

    Article  Google Scholar 

  31. Basha AA, Vivekanandan S (2017) Optimal control identification of IMC and PID controllers for insulin infusion. In: Proceedings of International Conference on Current Trends in Computer, Electrical, Electronics and Communication, pp 679–682

  32. Varadharajan R, Priyan MK, Panchatcharam P, Vivekanandan S, Gunasekaran M (2018) A new approach for prediction of lung carcinoma using back propagation neural network with decision tree classifiers. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-018-1066-y

    Article  Google Scholar 

  33. Sherr J, Tamborlane W (2008) Past, present, and future if insulin pump therapy: a better shot at diabetes control. Mt Sinai J Med 75:352–361

    Article  Google Scholar 

  34. Al-Tabakha MM, Arida AI (2008) Recent challenges in insulin delivery systems: a review. Indian J Pharm Sci 70:278–286

    Article  Google Scholar 

  35. Mathan K, Kumar PM, Panchatcharam P, Manogaran G, Varadharajan R (2018) A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Des Autom Embed Syst 22:225–242. https://doi.org/10.1007/s10617-018-9205-4

    Article  Google Scholar 

  36. Driankov D, Hellendoorn H, Reinfrank M (1996) An introduction to fuzzy control, 2nd edn. Springer, Berlin

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632 014, India

    A. Alavudeen Basha & S. Vivekanandan

Authors
  1. A. Alavudeen Basha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Vivekanandan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Alavudeen Basha.

Ethics declarations

Conflict of interest

There is no conflict of interest from both the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alavudeen Basha, A., Vivekanandan, S. A fuzzy-based adaptive multi-input–output scheme in lieu of diabetic and hypertension management for post-operative patients: an human–machine interface approach with its continuum. Neural Comput & Applic 34, 13407–13423 (2022). https://doi.org/10.1007/s00521-020-04975-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04975-8

Keywords

Search

Navigation