Skip to main content

Advertisement

Log in

Blood Glucose Regulation for Post-Operative Patients with Diabetics and Hypertension Continuum: A Cascade Control-Based Approach

  • Patient Facing Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Management of glycemic level in post-operative condition is critical for hypertensive patients and the post-operative stress may results in hyperglycemia, hyper insulin and osmotic diuresis. Recent medical research shows that diabetic and hypertension hands together in a significant overlap in its etiology and its disease mechanism. It is clear that there is a call for monitoring in the parameter and controlling the glucose level particularly in the presence of hypertension. This paper proposes the novel complex (cascade) control system to control the insulin infusion level particularly in the presence of hypertension. Based on the requirements the structure has been designed and the simulation results indicates that the proposed control strategy shows better results and may achieve potentially better glycemic control to the hypersensitive diabetic patients.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Wild, S., Roglic, G., Green, A., Sicree, R., and King, H., Global Prevalence of Diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care 27(5):1,047–1,053, 2004.

    Article  Google Scholar 

  2. Kumar, P. M., Lokesh, S., Varatharajan, R., Babu, G. C., and Parthasarathy, P., Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Futur. Gener. Comput. Syst. 86:527–534, 2018.

    Article  Google Scholar 

  3. Kumar, P. M., Devi, U., Manogaran, G., Sundarasekar, R., Chilamkurti, N., and Varatharajan, R., Ant colony optimization algorithm with Internet of Vehicles for intelligent traffic control system. Comput. Netw. 144:154–162, 2018.

    Article  Google Scholar 

  4. Basha, A., and Vivekanandan, S., Evolution of diabetic control identification in lieu of continuous glucose monitoring technology- A Review. Int. J. Appl. Eng. Res. 12(16):6102–6107, 2017.

    Google Scholar 

  5. Gurushankar, G., Sowers, J. R., et al., Hypertension and diabetes mellitus. Reference Section: European Cardiovascular Disease. pp. 01–07, 2006.

  6. Mugo MN, Stump CS, Rao PG, et al., Hypertension and diabetes mellitus. In: Black HR, Elliott WJ (eds). Hypertension: A Companion to Braunwald's Heart Disease. Elsevier, p. 409, 2007.

  7. Mathan, K., Kumar, P. M., Panchatcharam, P., Manogaran, G., & Varadharajan, R., A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Design Automation for Embedded Systems. 1–18, 2018.

  8. Priya, S., Varatharajan, R., Manogaran, G., Sundarasekar, R., and Kumar, P. M., Paillier homomorphic cryptosystem with poker shuffling transformation based water marking method for the secured transmission of digital medical images. Personal and Ubiquitous Computing. 1–11, 2018.

  9. Varatharajan, R., Preethi, A. P., Manogaran, G., Kumar, P. M., and Sundarasekar, R., Stealthy attack detection in multi-channel multi-radio wireless networks. Multimedia Tools and Applications. 1–24, 2018.

  10. Landsberg, L., MolitchM. Diabetes and hypertension: Pathogenesis,prevention and treatment. Clin. Exp. Hypertens. 26:621–628, 2004.

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  12. Sowers, J. R., Epstein, M., and Frohlich, E., D, “Diabetes, hypertension, and cardiovascular disease: An update”. Hypertension 37(4):1,053–1,059, 2001.

    Article  CAS  Google Scholar 

  13. Dagogo-Jack, S., Management of Diabetes Mellitus in Surgical Patients. Diabetes Spectrum 15(1):44–48, 2002.

    Article  Google Scholar 

  14. American Diabetes Association, Standards of medical care for patients with diabetes mellitus (Position Statement). Diabetes Care 24(Suppl. 1):S33–S43, 2001.

    Google Scholar 

  15. Parthasarathy, P., and Vivekanandan, S., A comprehensive review on thin film-based nano-biosensor for uric acid determination: Arthritis diagnosis. World Review of Science, Technology and Sustainable Development 14(1):52–71, 2018.

    Article  Google Scholar 

  16. Parthasarathy, P., and Vivekanandan, S., Urate crystal deposition, prevention and various diagnosis techniques of GOUT arthritis disease: A comprehensive review. Health Information Science and Systems 6(1):19, 2018.

    Article  Google Scholar 

  17. Ganesh, J., and Viswanathan, V., Management of diabetic hypertensives. Indian Journal of Endocrinology and Metabolism. 15(Supplement 4):374–379, 2011.

    Google Scholar 

  18. American Diabetes Association, Diagnosis and classification of diabetes mellitus. Diabetes Care 34:S5–S10, 2011.

    Google Scholar 

  19. Parthasarathy, P., and Vivekanandan, S., Investigation on uric acid biosensor model for enzyme layer thickness for the application of arthritis disease diagnosis. Health Information Science and Systems 6(1):–6, 2018.

  20. Parthasarathy, P., and Vivekanandan, S., A typical IoT architecture-based regular monitoring of arthritis disease using time wrapping algorithm. International Journal of Computers and Applications. 1–11, 2018.

  21. Breithaupt, T., Postoperative glycemic control in cardiac surgery patients. Harmacology Notes. pp. 79–82, 2010.

  22. Libman, I. M., and Becker, D. J., Coexistence of type 1 and type 2 diabetes mellitus: "Double" diabetes? Pediatr. Diabetes 4:110–113, 2003.

    Article  Google Scholar 

  23. Vivekanandan, S., and Devanand, M., Remote monitoring for diabetes disorder: Pilot study using InDiaTel prototype. European Research in Telemedicine/La Recherche Européenne en Télémédecine 4:63–69, 2015.

    Article  Google Scholar 

  24. Bergman, R. N., and Urquhart, J., The pilot gland approach to the study of insulin secretory dynamics. Recent Prog. Horm. Res. 27:583–605, 1971 passim.

    CAS  PubMed  Google Scholar 

  25. Kovács, L., Szalay, P., Benyó, B., and Chase, G., Applicability results of a nonlinear model-based robust blood glucose control algorithm. J. Diabetes Sci. Technol. 7:708–716.

  26. Varadharajan, R., Priyan, M. K., Panchatcharam, P., Vivekanandan, S., and Gunasekaran, M., A new approach for prediction of lung carcinoma using back propagation neural network with decision tree classifiers. Journal of Ambient Intelligence and Humanized Computing. 1–12, 2018.

  27. Basha, A. A., Vivekanandan, S., and Parthasarathy, P., Evolution of blood pressure control identification in lieu of post-surgery diabetic patients: A review. Health Information Science and Systems 6(1):17, 2018.

    Article  Google Scholar 

  28. Magni, L., Raimondo, D. M., Bossi, L., Man, C. D., De Nicolao, G. et al., Model predictive control of type 1 diabetes: An in silico trial. J. Diabetes Sci. Technol. 1:804–812, 2007.

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. Al-Tabakha, M. M., and Arida, A. I., Recent challenges in insulin delivery systems: A review. Indian J. Pharm. Sci. 70:278–286, 2008.

    Article  CAS  Google Scholar 

  31. Slate J. B., Sheppard L. C., Rideout V. C., et al., Closed-loop ni-troprusside infusion: Modeling and control theory for clinical application. Proceedings of IEEE International Symposium on Circuits Systems, pp. 482–488, 1980.

  32. Slate, J. B., Sheppard, L. C., Rideout, V. C., and Blackstone, E. H., A model for design of a blood pressure controller for hypertensive patients. In: Proc IEEE EMBS Conf. pp. 867–72, 1979.

  33. Basha, A., Vivekanandan, S., Optimal control identification of IMC and PID controllers for insulin infusion. IEEE Conference Transactions. 2017.

  34. Garcia, C. E., and Morari, M. A., Internal model control. Unifying review and Some New Results. Industrial and Engineering Chemistry and Process Design Development 21(2):308–323, 1982.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Alavudeen Basha.

Additional information

Publisher’s Note

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

This article is part of the Topical Collection on Patient Facing Systems

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Basha, A.A., Vivekanandan, S. & Parthasarathy, P. Blood Glucose Regulation for Post-Operative Patients with Diabetics and Hypertension Continuum: A Cascade Control-Based Approach. J Med Syst 43, 95 (2019). https://doi.org/10.1007/s10916-019-1224-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-019-1224-6

Keywords

Navigation