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Performance Assessment of Model-Based Artificial Pancreas Control Systems

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Prediction Methods for Blood Glucose Concentration

Part of the book series: Lecture Notes in Bioengineering ((LNBE))

Abstract

Many artificial pancreas control systems are based on models that predict glucose concentrations. The performance of these control systems depends on the accuracy of the models and may be affected when large dynamic changes in the human body or changes in equipment performance occur and move the operating conditions away from those used in developing the models and designing the control system. A controller performance assessment (CPA) module is developed to evaluate the performance of model-based controllers and initiate controller retuning if there is significant performance deterioration. The generalized predictive control (GPC) approach that utilizes models for glucose concentration predictions is used for illustrating the performance of the CPA. The module has six indexes that capture different aspects of model and controller performance, which can be analyzed to determine the specific component of the controller that caused performance deterioration. Four different kinds of controller errors were diagnosed by indexes and used for controller retuning. Thirty subjects in the UVa/Padova metabolic simulator are used in simulations to evaluate the performance of the CPA module. The results indicate that a GPC with the proposed CPA module has a safer range of glucose concentration variation and more reasonable insulin suggestions than a GPC without controller retuning guided by the CPA module.

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References

  1. Atlas, E., Nimri, R., Miller, S., Grunberg, E.A., Phillip, M.: MD-logic artificial pancreas system: a pilot study in adults with type 1 diabetes. Diabetes Care 33(5), 1072–1076 (2010)

    Article  Google Scholar 

  2. Bequette, B.W.: Challenges and recent progress in the development of a closed-loop artificial pancreas. Annu. Rev. Control 36(2), 255–266 (2012)

    Article  Google Scholar 

  3. Bequette, B.W.: Fault detection and safety in closed-loop artificial pancreas systems. J. Diabetes Sci. Technol. 8(6), 1204–1214 (2014)

    Article  Google Scholar 

  4. Breton, M., Farret, A., Bruttomesso, D., Anderson, S., Magni, L., Patek, S., Dalla Man, C., Place, J., Demartini, S., Del Favero, S., Toffanin, C., Hughes-Karvetski, C., Dassau, E., Zisser, H., Doyle, F.J., De Nicolao, G., Avogaro, A., Cobelli, C., Renard, E., Kovatchev, B.: Fully integrated artificial pancreas in type 1 diabetes: modular closed-loop glucose control maintains near normoglycemia. Diabetes 61(9), 2230–2237 (2012)

    Google Scholar 

  5. Cinar, A., Palazoglu, A., Kayihan, F.: Chemical Process Performance Evaluation. CRC Press, Boca Raton (2007)

    Google Scholar 

  6. Desborough, L., Harris, T.: Performance assessment measures for univariate feedback control. Can. J. Chem. Eng. 6(70), 1186–1197 (1992)

    Article  Google Scholar 

  7. Doyle, F.J., Huyett, L.M., Lee, J.B., Zisser, H.C., Dassau, E.: Closed-loop artificial pancreas systems: engineering the algorithms. Diabetes Care 37(5), 1191–1197 (2014)

    Article  Google Scholar 

  8. El-Khatib, F.H., Russell, S.J., Magyar, K.L., Sinha, M., McKeon, K., Nathan, D.M., Damiano, E.R.: Autonomous and continuous adaptation of a bihormonal bionic pancreas in adults and adolescents with type 1 diabetes. J. Clin. Endocrinol. Metab. 99(5), 1701–1711 (2014)

    Article  Google Scholar 

  9. Eren-Oruklu, M., Cinar, A., Quinn, L., Smith, D.: Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes. J. Process control 19(8), 1333–1346 (2009)

    Article  Google Scholar 

  10. Gorton, I., Gracio, D.: Data-Intensive Computing: Architectures, Algorithms, and Applications. Cambridge University Press, New York (2012)

    Book  Google Scholar 

  11. Harvey, R.A., Dassau, E., Bevier, W.C., Seborg, D.E., Jovanovič, L., Doyle, F.J., Zisser, H.C.: Clinical evaluation of an automated artificial pancreas using zone-model predictive control and health monitoring system. Diabetes Technol. Ther. 16(6), 348–357 (2014)

    Article  Google Scholar 

  12. Huang, B.: Bayesian methods for control loop monitoring and diagnosis. J. Process Control 9(18), 829–838 (2008)

    Article  Google Scholar 

  13. Huang, B., Shah, S.: Performance Assessment of Control Loops: Theory and Applications. Springer Science & Business Media, London (1999)

    Book  Google Scholar 

  14. Jelali, M.: An overview of control performance assessment technology and industrial applications. Control Eng. Pract. 5(14), 441–466 (2006)

    Article  Google Scholar 

  15. Kendra, S., Cinar, A.: Controller performance assessment by frequency domain techniques. J. Process Control 3(7), 181–194 (1997)

    Google Scholar 

  16. Kendra, S.J., Basila, M.R., Cinar, A.: A supervisory KBS for real—time monitoring and modification of multivariable controllers for continuous processes. Methods and Applications of Intelligent Control, pp. 139–171. Kluwer academic publishers, Norwell (1997)

    Chapter  Google Scholar 

  17. Kovatchev, B.P., Breton, M., Man, C.D., Cobelli, C.: In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes. J. Diabetes Sci. Technol. 3(1), 44–55 (2009)

    Article  Google Scholar 

  18. Leal, Y., Ruiz, M., Lorencio, C., Bondia, J., Mujica, L., Vehi, J.: Principal component analysis in combination with case-based reasoning for detecting therapeutically correct and incorrect measurements in continuous glucose monitoring systems. Biomed. Signal Process. Control 6(8), 603–614 (2013)

    Article  Google Scholar 

  19. Loquasto, F., Seborg, D.: Monitoring model predictive control systems using pattern classification and neural networks. Ind. Eng. Chem. Res. 20(42), 4689–4701 (2003)

    Article  Google Scholar 

  20. Luijf, Y.M., DeVries, J.H., Zwinderman, K., Leelarathna, L., Nodale, M., Caldwell, K., Kumareswaran, K., Elleri, D., Allen, J.M., Wilinska, M.E., Evans, M.L., Hovorka, R., Doll, W., Ellmerer, M., Mader, J.K., Renard, E., Place, J., Farret, A., Cobelli, C., Del Favero, S., Dalla Man, C., Avogaro, A., Bruttomesso, D., Filippi, A., Scotton, R., Magni, L., Lanzola, G., Di Palma, F., Soru, P., Toffanin, C., De Nicolao, G., Arnolds, S., Benesch, C., Heinemann, L.: Day and night closed-loop control in adults with type 1 diabetes: a comparison of two closed-loop algorithms driving continuous subcutaneous insulin infusion versus patient self-management. Diabetes Care 36(12), 3882–3887 (2013)

    Google Scholar 

  21. Mauseth, R., Wang, Y., Dassau, E., Kircher, R., Matheson, D., Zisser, H., Jovanovic, L., Doyle, F.J.: Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor. J. Diabetes Sci. Technol. 4(4), 913–922 (2010)

    Article  Google Scholar 

  22. Miller, R., Desborough, L., Timmons, C.: Citgo’s experience with controller performance assessment. In: Proceedings of the NPRA 1998 Computer Conference (San Antonio,TX, USA 1998)

    Google Scholar 

  23. Paulonis, M., Cox, J.: A practical approach for large-scale controller performance assessment, diagnosis, and improvement. J. Process Control 2(13), 155–168 (2003)

    Article  Google Scholar 

  24. Qin, S., Yu, J.: Recent developments in multivariable controller performance monitoring. J. Process Control 3(17), 221–227 (2007)

    Google Scholar 

  25. Renard, E., Place, J., Cantwell, M., Chevassus, H., Palerm, C.C.: Closed-loop insulin delivery using a subcutaneous glucose sensor and intraperitoneal insulin delivery: feasibility study testing a new model for the artificial pancreas. Diabetes Care 33(1), 121–127 (2010)

    Article  Google Scholar 

  26. Schäfer, J., Cinar, A.: Multivariable MPC system performance assessment, monitoring, and diagnosis. J. Process Control 2(14), 113–129 (2004)

    Article  Google Scholar 

  27. Sherr, J.L., Cengiz, E., Palerm, C.C., Clark, B., Kurtz, N., Roy, A., Carria, L., Cantwell, M., Tamborlane, W.V., Weinzimer, S.A.: Reduced hypoglycemia and increased time in target using closed-loop insulin delivery during nights with or without antecedent afternoon exercise in type 1 diabetes. Diabetes Care 36(10), 2909–2914 (2013)

    Article  Google Scholar 

  28. Stanfelj, N., Marlin, T., MacGregor, J.: Monitoring and diagnosing process control performance: the single-loop case. Ind. Eng. Chem. Res. 2(32), 301–314 (1993)

    Article  Google Scholar 

  29. Steil, G., Rebrin, K., Mastrototaro, J.J.: Metabolic modelling and the closed-loop insulin delivery problem. Diabetes Res. Clin. Prac. 74(Suppl 2), S183–186 (2006)

    Google Scholar 

  30. Steil, G.M., Palerm, C.C., Kurtz, N., Voskanyan, G., Roy, A., Paz, S., Kandeel, F.R.: The effect of insulin feedback on closed loop glucose control. J. Clin. Endocrinol. Metab. 96(5), 1402–1408 (2011)

    Article  Google Scholar 

  31. Thornhill, N., Horch, A.: Advances and new directions in plant-wide disturbance detection and diagnosis. Control Eng. Prac. 10(15), 1196–1206 (2007)

    Article  Google Scholar 

  32. Tian, X., Chen, G., Cao, Y., Chen, S.: Performance monitoring of mpc based on dynamic principal component analysis. In: Preprints of the 18th IFAC World Congress (2011)

    Google Scholar 

  33. Turksoy, K., Cinar, A.: Adaptive control of artificial pancreas systems—a review. J. Healthc. Eng. 5(1), 1–22 (2014)

    Article  Google Scholar 

  34. Turksoy, K., Quinn, L., Littlejohn, E., Cinar, A.: Multivariable adaptive identification and control for artificial pancreas systems. IEEE Trans. Biomed. Eng. 61(3), 883–891 (2014)

    Article  Google Scholar 

  35. Turksoy, K., Quinn, L.T., Littlejohn, E., Cinar, A.: An integrated multivariable artificial pancreas control system. J. Diabetes Sci. Technol. 8(3), 498–507 (2014)

    Article  Google Scholar 

  36. Walsh, J., Roberts, R.: Pumping Insulin. Torrey Pines Press, San Diego (2006)

    Google Scholar 

Download references

Acknowledgments

This work is supported by the National Institutes of Health (NIH) under grants 1DP3DK101077-01 and 1DP3DK101075-01 and the Juvenile Diabetes Research Foundation International (JDRF) under grant 17-2013-472.

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Authors and Affiliations

  1. Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA

    Jianyuan Feng & Ali Cinar

  2. Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA

    Kamuran Turksoy & Ali Cinar

Authors
  1. Jianyuan Feng
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  2. Kamuran Turksoy
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  3. Ali Cinar
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Correspondence to Ali Cinar .

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Editors and Affiliations

  1. Institute for Design and Control of Mechatronical Systems, Johannes Kepler University Linz, Linz, Austria

    Harald Kirchsteiger

  2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark

    John Bagterp Jørgensen

  3. Institut de Génomique Fonctionnelle de Montpellier, Montpellier cedex 5, France

    Eric Renard

  4. Institute for Design and Control of Mechatronical Systems, Johannes Kepler University Linz, Linz, Austria

    Luigi del Re

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Feng, J., Turksoy, K., Cinar, A. (2016). Performance Assessment of Model-Based Artificial Pancreas Control Systems. In: Kirchsteiger, H., Jørgensen, J., Renard, E., del Re, L. (eds) Prediction Methods for Blood Glucose Concentration. Lecture Notes in Bioengineering. Springer, Cham. https://doi.org/10.1007/978-3-319-25913-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-25913-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25911-6

  • Online ISBN: 978-3-319-25913-0

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