Neural Network and Physiological Parameters Based Control of Artificial Pancreas for Improved Patient Safety

  • Saad Bin Qaisar
  • Salman H. Khan
  • Sahar Imtiaz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7335)

Abstract

Acyber-physical system (CPS) establishes a close interaction between system’s computational core and the control of physical process. In case of Diabetes, failure in endogenous insulin production requires exogenous infusion of required drug amount. We have proposed an architecture for artificial pancreas and checked its validity in simulations. The aim is to control blood glucose level (BGL) of a patient suffering from diabetes and to prevent the harmful state of Hypoglycemia. For this, vital signs monitoring is introduced through which hypoglycemic condition can be efficiently detected and avoided. Electrocardiogram, Heart beat rate, Electroencephalography and skin resistance are known to depict an irregularity in blood glucose. Upon detection, a specified amount of Glucagon is infused into patient’s body. The system consists of an insulin infusion and glucagon pump, through which insulin/glucagon is entered into the patient’s body subcutaneously, based on the current BGL. A neural network predictive controller is designed to keep the glucose level inside the desired ’safe range’. The simulations have shown that patient safety can be improved through this strategy.

Keywords

diabetes insulin infusion exogenous endogenous feedback control simulation neural network 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lee, I., Sokolsky, O.: Medical Cyber Physical Systems. In: Proc. of DAC, Anaheim, California, USA (2010)Google Scholar
  2. 2.
    High Confidence Software and Systems Coordinating Group. High-confidence medical devices: Cyber-physical systems for 21st century health care. A Research and Development Needs Report, NCO/NITRD (February 2009)Google Scholar
  3. 3.
    Bergman, R.N., Phillips, L.S., Cobelli, C.: Physiologic evaluation of factors controlling glucose tolerance in man. Measurement of insulin sensitivity and β -cell glucose sensitivity from the response to intravenous glucose. Journal of Clinical Investigation 68(6), 1456–1467 (1981)CrossRefGoogle Scholar
  4. 4.
    Dua, P., Doyle, F.J., Pistikopoulos: Model-Based Blood Glucose Control for Type 1 Diabetes via Parametric Programming. IEEE Transactions on Biomedical Engineering 53, 1478–1491 (2006)CrossRefGoogle Scholar
  5. 5.
    Diabetes Control and Complications Research Group, The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N. Engl. J. Med. 329(14), 977–986 (1993) Google Scholar
  6. 6.
    Banting, F.G., Best, C.H., Collip, J.B., Campbell, W.R., Fletcher, A.A.: Pancreatic extracts in the treatment of diabetes mellitus: preliminary report. Cmaj 145(10), 1281–1286 (1922) (reprinted 1991)Google Scholar
  7. 7.
    Cryer, P.E.: Hypoglycaemia: the limiting factor in the glycaemic management of Type I and Type II diabetes. Diabetologia 45(7), 937–948 (2002)CrossRefGoogle Scholar
  8. 8.
    Hovorka, R., Chassin, L.J., Wilinska, M.E., Canonico, V., Akwi, J.A., Federici, M.O., Massi- Benedetti, M., Hutzli, I., Zaugg, C., Kaufmann, H., Both, M., Vering, T., Schaller, H.C., Schaupp, L., Bodenlenz, M., Pieber, T.R.: Closing the loop: the adicol experience. Diabetes Technol. Ther. 6(3), 307–318 (2004)CrossRefGoogle Scholar
  9. 9.
    Farmer Jr., T.G., Edgar, T.F., Peppas, N.A.: The future of open- and closed-loop insulin delivery systems. J. Pharm. Pharmacol. 60(1), 1–13 (2008)CrossRefGoogle Scholar
  10. 10.
    El Youssef, J., Castle, J., Kenneth Ward, W.: A Review of Closed-Loop Algorithms for Glycemic Control in the Treatment of Type 1 Diabetes. Algorithms 2, 518–532 (2009)CrossRefGoogle Scholar
  11. 11.
    Weinzimer, S.A., Steil, G.M., Swan, K.L., Dziura, J., Kurtz, N., Tamborlane, W.V.: Fully automated closed-loop insulin delivery versus semiautomated hybrid control in pediatric patients with type 1 diabetes using an artificial pancreas. Diabetes Care 31(5), 934–939 (2008)CrossRefGoogle Scholar
  12. 12.
    Elbein, S.C., Wegner, K., Kahn, S.E.: Reduced beta-cell compensation to the insulin resistance associated with obesity in members of caucasian familial type 2 diabetic kindreds. Diabetes Care 23(2), 221–227 (2000)CrossRefGoogle Scholar
  13. 13.
    Bellazzi, R., Nucci, G., Cobelli, C.: The subcutaneous route to insulindependent diabetes therapy. IEEE Engineering in Medicine and Biology 20, 54–64 (2001)CrossRefGoogle Scholar
  14. 14.
    Gómez, E.J., Pérez, M.E.H., Vering, T.: The INCA System: A Further Step Towards a Telemedical Artificial Pancreas. IEEE Transactions on Information Technology In Biomedicine 12(4) (2008)Google Scholar
  15. 15.
    World Health Organization, Fact Sheet No. 138 (April 20, 2011) (Online), http://www.who.int/mediacentre/factsheets/fs138/en/
  16. 16.
    Carson, E.R., Cobelli, C. (eds.): Modelling Methodology for Physiology and Medicine. Academic Press, San Diego (2001)Google Scholar
  17. 17.
    Parker, R.S., Doyle III, F.J., Peppas, N.A.: The intravenous route to blood glucose control. IEEE Eng. Med. Biol. Mag. 20(1), 65–73 (2001)CrossRefGoogle Scholar
  18. 18.
    Bellazzi, R., Nucci, G., Cobelli, C.: The subcutaneous route to insulin-dependent diabetes therapy. IEEE Eng. Med. Biol. Mag. 20(1), 54–64 (2001)CrossRefGoogle Scholar
  19. 19.
    Hovorka, R.: Continuous glucose monitoring and closed-loop systems. Diabet. Med. 23, 1–12 (2005)CrossRefGoogle Scholar
  20. 20.
    Bequette, B.W.: A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas. Diabetes Technol. Ther. 7(1), 28–47 (2005)CrossRefGoogle Scholar
  21. 21.
    Owens, C., Zisser, H., Jovanovic, L., Srinivasan, B., Bonvin, D., Doyle III, J.: Run-to-run control of blood glucose concentrations for people with type 1 diabetes mellitus. IEEE Trans. Biomed. Eng. 53(6), 996–1005 (2006)CrossRefGoogle Scholar
  22. 22.
    Dudde, R., Vering, T., Piechotta, G., Hintsche, R.: Computer-aidedcontinuous drug infusion: setup and test of a mobile closed-loop system for the continuous automated infusion of insulin. IEEE Trans. Inf. Technol. Biomed. 10(2), 395–402 (2006)CrossRefGoogle Scholar
  23. 23.
    Phee, H.K., Tung, W.L., Quek, C.: A personalised approach to insulin regulation using brain inspired neural semantic memory in biabetic glucose control. In: IEEE CEC (2007)Google Scholar
  24. 24.
    Hovorka, R.: The future of continuous glucose monitoring: closed-loop. Current Diabetes Reviews 4(3), 269–279 (2008)CrossRefGoogle Scholar
  25. 25.
    Chee, E., Fernando, T., van Heerden, P.V.: Simulation study on automatic blood glucose control. In: 7th Australian and New Zealand Intelligent Information Systems Conference (2001)Google Scholar
  26. 26.
    Demuth, H., Beale, M.: Neural Network Toolbox for use with MATLAB, MathworksGoogle Scholar
  27. 27.
    Renard, E., Costalat, G., Chevassus, H., Bringer, J.: Artificial beta-cell: clinical experience toward an implantable closed-loop insulin delivery system. Diabetes Metab. 32(5 Pt 2), 497–502 (2006)CrossRefGoogle Scholar
  28. 28.
    Chee, F., Savkin, A.V., Fernando, T.L., Nahavandi, S.: Optimal H∞ insulin injection control for blood glucose regulation in diabetic patients. IEEE Transactions on Biomedical Engineering 52(10), 1625–1631 (2005)CrossRefGoogle Scholar
  29. 29.
    Chen, J., Cao, K., Sun, Y., Xiao, Y., Su (Kevin), X.: Continuous Drug Infusion for Diabetes Therapy:A Closed-Loop Control System Design. EURASIP Journal onWireless Communications and Networking, Article ID 495185 (2008)Google Scholar
  30. 30.
    El Jabali, A.K.: Neural network modeling and control of type 1 diabetes melli- tus. Bioprocess Biosyst. Eng. 27, 75–79 (2005)CrossRefGoogle Scholar
  31. 31.
    Matlab Simulink R2011a Documentation (Online), http://www.mathworks.com/help/toolbox/slcontrol/ug/br684zf.html
  32. 32.
    David C. Klonoff, The Artificial Pancreas: How Sweet Engineering Will Solve Bitter Problems, Journal of Diabetes Science and Technology, 2007, Volume 1, Issue 1. Google Scholar
  33. 33.
    Hung T. Nguyen, N.Ghevondian, and T. W. Jones , Real-time Detection of Nocturnal Hypoglycemic Episodes using a Novel Non-invasive Hypoglycemia Monitor , 31st Annual International Conference of the IEEE EMBS ,2009. Google Scholar
  34. 34.
    Heller, S.R., Macdonald, I.A.: Physiological disturbances in hypoglycemia: effect on subjective awareness. Clin. Sci. 81, 1–9 (1991)Google Scholar
  35. 35.
    Gale, E.A.M., Bennett, T., MacDonald, I.A., Holst, J.J., Matthews, J.A.: The physiological effects of insulin-induced hypoglycemia in man: responses at differing levels of blood glucose. Clin. Sciences 65, 263–271 (1983)Google Scholar
  36. 36.
    Marques, J.L., et al.: Altered ventricular repolarisation during hypoglycaemic in patient with diabetes. Diabetic Med. 8, 648–654 (1997)CrossRefGoogle Scholar
  37. 37.
    Koivikko, M.L., Salmela, P.I., et al.: Effects of Sustained Insulin-Induced Hypoglycemia on Cardiovascular Autonomic Regulation in Type 1 Diabetes. Diabetes 54, 745–750 (2005)CrossRefGoogle Scholar
  38. 38.
    Castle, J.R., Engle, J.M., Youseff, J.E., et al.: Novel Use of Glucagon in a Closed-Loop System for Prevention of Hypoglycemia in Type 1 Diabetes. Diabetes Care 33(6), 1282–1287 (2010)CrossRefGoogle Scholar
  39. 39.
    Heger, G., Howorka, K., Thoma, H., Tribl, G., Zeitlhofer, J.: Monitoring setup for selection of parameters for detection of hypoglycemia in diabetic patients. Medical & Biological Engineering & Computing (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Saad Bin Qaisar
    • 1
  • Salman H. Khan
    • 1
  • Sahar Imtiaz
    • 1
  1. 1.Department of Electrical EngineeringNational University of Sciences and TechnologyIslamabadPakistan

Personalised recommendations