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Journal of Electrical and Computer Engineering
Volume 2011, Article ID 681786, 11 pages
http://dx.doi.org/10.1155/2011/681786
Research Article

Blood Glucose Prediction Using Artificial Neural Networks Trained with the AIDA Diabetes Simulator: A Proof-of-Concept Pilot Study

1Bioengineering Department, University of Strathclyde, Wolfson Building, 106 Rottenrow, Glasgow G4 0NW, UK
2Department of Imaging, CMRU, Imperial College of Science, Technology and Medicine, Royal Brompton Hospital, Sydney Street, London SW3 6NP, UK
3Scotsig, 40 Westbourne Gardens, Glasgow G12 9PF, UK
4Ateeda Limited, CBC House, 24 Canning Street, Edinburgh EH3 8EG, UK

Received 3 November 2010; Accepted 18 February 2011

Academic Editor: John Walsh

Copyright © 2011 Gavin Robertson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Wild, G. Roglic, A. Green, R. Sicree, and H. King, “Global prevalence of diabetes: estimates for the year 2000 and projections for 2030,” Diabetes Care, vol. 27, no. 5, pp. 1047–1053, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. H. R. Murphy, D. Elleri, J. M. Allen et al., “Closed-loop insulin delivery during pregnancy complicated by type 1 diabetes,” Diabetes Care, vol. 34, no. 2, pp. 406–411, 2011. View at Publisher · View at Google Scholar
  3. M. W. Percival, H. Zisser, L . Jovanovic, and F. J. Doyle 3rd, “Closed-loop control and advisory mode evaluation of an artificial pancreatic Beta cell: use of proportional-integral-derivative equivalent model-based controllers,” Journal of Diabetes Science and Technology, vol. 2, no. 4, pp. 636–644, 2008. View at Google Scholar
  4. B. Grosman, E. Dassau, H. C. Zisser, L. Jovanovic, and F. J. Doyle 3rd, “Zone model predictive control: a strategy to minimize hyper- and hypoglycemic events,” Journal of Diabetes Science and Technology, vol. 4, no. 4, pp. 961–975, 2010. View at Google Scholar
  5. J. J. Cordingley, D. Vlasselaers, N. C. Dormand et al., “Intensive insulin therapy: enhanced Model Predictive Control algorithm versus standard care,” Intensive Care Medicine, vol. 35, no. 1, pp. 123–128, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. M. E. Wilinska, E. S. Budiman, M. B. Taub et al., “Overnight closed-loop insulin delivery with model predictive control: assessment of hypoglycemia and hyperglycemia risk using simulation studies,” Journal of Diabetes Science and Technology, vol. 3, no. 5, pp. 1109–1120, 2009. View at Google Scholar · View at Scopus
  7. R. Hovorka, “The future of continuous glucose monitoring: closed loop,” Current Diabetes Reviews, vol. 4, no. 3, pp. 269–279, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Haykin, Neural Networks and Learning Machines, Pearson Education Inc, Upper Saddle River, NJ, USA, 3rd edition, 2009.
  9. W. Sandham, D. Nikoletou, D. Hamilton, K. Patterson, A. Japp, and C. Macgregor, “Blood glucose prediction for diabetes therapy using a recurrent artificial neural network,” in Proceedings of the 9th European Signal Processing Conference (EUSIPCO '98), vol. 11, pp. 673–676, 1998.
  10. S. G. Mougiakakou, K. Prountzou, and K. S. Nikita, “A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS '05), vol. 1, pp. 298–301, September 2005. View at Scopus
  11. S. G. Mougiakakou, A. Prountzou, D. Iliopoulou, K. S. Nikita, A. Vazeou, and C. S. Bartsocas, “Neural network based glucose - Insulin metabolism models for children with type 1 diabetes,” in Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '06), pp. 3545–3548, September 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. K. Zarkogianni, S. G. Mougiakakou, A. Prountzou, A. Vazeou, C. S. Bartsocas, and K. S. Nikita, “An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5972–5975, 2007. View at Scopus
  13. S. M. Pappada, B. D. Cameron, and P. M. Rosman, “Development of a neural network for prediction of glucose concentration in type 1 diabetes patients,” Journal of Diabetes Science and Technology, vol. 2, no. 5, pp. 792–801, 2008. View at Google Scholar
  14. C. Pérez-Gandía, A. Facchinetti, G. Sparacino et al., “Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring,” Diabetes Technology and Therapeutics, vol. 12, no. 1, pp. 81–88, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. K. Curran, E. Nichols, E. Xie, and R. Harper, “An intensive insulinotherapy mobile phone application built on artificial intelligence techniques,” Journal of Diabetes Science and Technology, vol. 4, no. 1, pp. 209–220, 2010. View at Google Scholar
  16. Z. Trajanoski and P. Wach, “Neural predictive controller for insulin delivery using the subcutaneous route,” IEEE Transactions on Biomedical Engineering, vol. 45, no. 9, pp. 1122–1134, 1998. View at Publisher · View at Google Scholar · View at Scopus
  17. V. Tresp, T. Briegel, and J. Moody, “Neural-network models for the blood glucose metabolism of a diabetic,” IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 1204–1213, 1999. View at Google Scholar · View at Scopus
  18. S. G. Mougiakakou and K. S. Nikita, “A neural network approach for insulin regime and dose adjustment in type 1 diabetes,” Diabetes Technology and Therapeutics, vol. 2, no. 3, pp. 381–389, 2000. View at Publisher · View at Google Scholar · View at Scopus
  19. E. Otto, C. Semotok, J. Andrysek, and O. Basir, “An intelligent diabetes software prototype: predicting blood glucose levels and recommending regimen changes,” Diabetes Technology and Therapeutics, vol. 2, no. 4, pp. 569–576, 2000. View at Publisher · View at Google Scholar · View at Scopus
  20. G. Gogou, N. Maglaveras, B. V. Ambrosiadou, D. Goulis, and C. Pappas, “A neural network approach in diabetes management by insulin administration,” Journal of Medical Systems, vol. 25, no. 2, pp. 119–131, 2001. View at Publisher · View at Google Scholar · View at Scopus
  21. W. Wang, Z. Z. Bian, L.-F. Yan, and J . Su, “A novel individual blood glucose control model based on mixture of experts neural networks,” in Proceedings of the International Symposium on Neural Networks (ISNN '04), F. Yin, , J. Wang, and C. Guo, Eds., Lecture Notes in Computer Science 3174, pp. 453–458, Springer, 2004.
  22. A. K. El-Jabali, “Neural network modeling and control of type 1 diabetes mellitus,” Bioprocess and Biosystems Engineering, vol. 27, no. 2, pp. 75–79, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. C. W. Ting and C. Quek, “A novel blood glucose regulation using TSK-FCMAC: a fuzzy CMAC based on the zero-ordered TSK fuzzy inference scheme,” IEEE Transactions on Neural Networks, vol. 20, no. 5, pp. 856–871, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. G. Baghdadi and A. M. Nasrabadi, “Controlling blood glucose levels in diabetics by neural network predictor,” in Proceedings of the 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society (EMBC '07), pp. 3216–3219, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. E. D. Lehmann and T. Deutsch, “A physiological model of glucose-insulin interaction in type 1 diabetes mellitus,” Journal of Biomedical Engineering, vol. 14, no. 3, pp. 235–242, 1992. View at Google Scholar · View at Scopus
  26. E. D. Lehmann, C. Tarín, J. Bondia, E. Teufel, and T. Deutsch, “Development of AIDA v4.3b diabetes simulator technical upgrade to support incorporation of lispro, aspart and glargine insulin analogues,” Journal of Electrical and Computer Engineering, vol. 2011, Article ID 427196, 17 pages, 2011. View at Google Scholar
  27. E. D. Lehmann, I. Hermanyi, and T. Deutsch, “Retrospective validation of a physiological model of glucose-insulin interaction in type 1 diabetes mellitus,” Medical Engineering and Physics, vol. 16, no. 3, pp. 193–202, 1994. View at Google Scholar · View at Scopus
  28. E. D. Lehmann, “Research use of the AIDA www.2aida.org diabetes software simulation program: a review—Part 1. Decision support testing and neural network training,” Diabetes Technology and Therapeutics, vol. 5, no. 3, pp. 425–438, 2003. View at Publisher · View at Google Scholar · View at Scopus
  29. E. D. Lehmann, “Research use of the AIDA www.2aida.org diabetes software simulation program: a review—Part 2. Generating simulated blood glucose data for prototype validation,” Diabetes Technology and Therapeutics, vol. 5, no. 4, pp. 641–651, 2003. View at Publisher · View at Google Scholar · View at Scopus