Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2013 (2013), Article ID 793869, 10 pages
http://dx.doi.org/10.1155/2013/793869
Research Article

Regularised Model Identification Improves Accuracy of Multisensor Systems for Noninvasive Continuous Glucose Monitoring in Diabetes Management

1Department of Information Engineering, University of Padova, Via Gradenigo 6B, 35131 Padova, Italy
2Biovotion AG, Technoparkstrasse 1, 8005 Zurich, Switzerland

Received 14 March 2013; Accepted 21 June 2013

Academic Editor: Kiwoon Kwon

Copyright © 2013 Mattia Zanon 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. M. Brownlee, “The pathobiology of diabetic complications: a unifying mechanism,” Diabetes, vol. 54, no. 6, pp. 1615–1625, 2005. View at Publisher · View at Google Scholar · View at Scopus
  2. IDF Diabetes Atlas, 5th edition, 2013, http://www.idf.org/diabetesatlas/.
  3. J. E. Shaw, R. A. Sicree, and P. Z. Zimmet, “Global estimates of the prevalence of diabetes for 2010 and 2030,” Diabetes Research and Clinical Practice, vol. 87, no. 1, pp. 4–14, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. B. W. Bode and T. Battelino, “Continuous glucose monitoring in 2010,” International Journal of Clinical Practice, Supplement, no. 170, pp. 10–15, 2011. View at Google Scholar · View at Scopus
  5. G. McGarraugh, “The chemistry of commercial continuous glucose monitors,” Diabetes Technology & Therapeutics, vol. 11, supplement 1, pp. S17–S24, 2009. View at Google Scholar · View at Scopus
  6. S. Garg, H. Zisser, S. Schwartz et al., “Improvement in glycemic excursions with a transcutaneous, real-time continuous glucose sensor: a randomized controlled trial,” Diabetes Care, vol. 29, no. 1, pp. 44–50, 2006. View at Google Scholar · View at Scopus
  7. T. Battelino and J. Bolinder, “Clinical use of real-time continuous glucose monitoring,” Current Diabetes Reviews, vol. 4, no. 3, pp. 218–222, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Sparacino, A. Facchinetti, A. Maran, and C. Cobelli, “Continuous glucose monitoring time series and hypo/hyperglycemia prevention: requirements, methods, open problems,” Current Diabetes Reviews, vol. 4, no. 3, pp. 181–192, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. C. Zecchin, A. Facchinetti, G. Sparacino, and C. Cobelli, “Reduction of number and duration of hypoglycemic events by glucose prediction methods: a proof-of-concept in silico study,” Diabetes Technology and Therapeutics, vol. 15, no. 1, pp. 66–77, 2013. View at Publisher · View at Google Scholar
  10. R. Hovorka, J. M. Allen, D. Elleri et al., “Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial,” The Lancet, vol. 375, no. 9716, pp. 743–751, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. S. J. Russell, F. H. El-Khatib, D. M. Nathan, K. L. Magyar, J. Jiang, and E. R. Damiano, “Blood glucose control in type 1 diabetes with a bihormonal bionic endocrine pancreas,” Diabetes Care, vol. 35, no. 11, pp. 2148–2155, 2012. View at Publisher · View at Google Scholar
  12. C. Cobelli, E. Renard, and B. Kovatchev, “Artificial pancreas: past, present, future,” Diabetes, vol. 60, no. 11, pp. 2672–2682, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Dauber, L. Corcia, J. Safer, M. S. Agus, S. Einis, and G. M. Steil, “Closed-loop insulin therapy improves glycemic control in children aged <7 years: a randomized controlled trial,” Diabetes Care, vol. 36, no. 2, pp. 222–227, 2013. View at Google Scholar
  14. M. Phillip, T. Battelino, E. Atlas et al., “Nocturnal glucose control with an artificial pancreas at a diabetes camp,” New England Journal of Medicine, vol. 368, no. 9, pp. 824–833, 2013. View at Publisher · View at Google Scholar
  15. A. Facchinetti, G. Sparacino, and C. Cobelli, “Online denoising method to handle intraindividual variability of signal-to-noise ratio in continuous glucose monitoring,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 9, pp. 2664–2671, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Guerra, A. Facchinetti, G. Sparacino, G. de Nicolao, and C. Cobelli, “Enhancing the accuracy of subcutaneous glucose sensors: a real-time deconvolution-based approach,” IEEE Transaction on Biomedical Engineering, vol. 59, no. 6, pp. 1658–1669, 2012. View at Publisher · View at Google Scholar
  17. A. Gani, A. V. Gribok, Y. Lu, W. K. Ward, R. A. Vigersky, and J. Reifman, “Universal glucose models for predicting subcutaneous glucose concentration in humans,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 1, pp. 157–165, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Eren-Oruklu, A. Cinar, and L. Quinn, “Hypoglycemia prediction with subject-specific recursive time-series models,” Journal of Diabetes Science and Technology, vol. 4, no. 1, pp. 25–33, 2010. View at Google Scholar · View at Scopus
  19. A. Facchinetti, G. Sparacino, E. Trifoglio, and C. Cobelli, “A new index to optimally design and compare continuous glucose monitoring glucose prediction algorithms,” Diabetes Technology and Therapeutics, vol. 13, no. 2, pp. 111–119, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. V. Naumova, S. V. Pereverzyev, and S. Sivananthan, “A meta-learning approach to the regularized learning-case study: blood glucose prediction,” Neural Networks, vol. 33, pp. 181–193, 2012. View at Publisher · View at Google Scholar
  21. C. Zecchin, A. Facchinetti, G. Sparacino, G. de Nicolao, and C. Cobelli, “Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 6, pp. 1550–1560, 2012. View at Publisher · View at Google Scholar
  22. G. Sparacino, A. Facchinetti, and C. Cobelli, “‘Smart’ continuous glucose monitoring sensors: on-line signal processing issues,” Sensors, vol. 10, no. 7, pp. 6751–6772, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. B. W. Bequette, “Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms,” Journal of Diabetes Science and Technology, vol. 4, no. 2, pp. 404–418, 2010. View at Google Scholar · View at Scopus
  24. A. Facchinetti, G. Sparacino, S. Guerra et al., “Real-time improvement of continuous glucose monitoring accuracy: the smart sensor concept,” Diabetes Care, vol. 36, no. 4, pp. 793–800, 2013. View at Publisher · View at Google Scholar
  25. A. Tura, “Advances in the development of devices for noninvasive glycemia monitoring: who will win the race?” Nutritional Therapy and Metabolism, vol. 28, no. 1, pp. 33–39, 2010. View at Google Scholar · View at Scopus
  26. S. K. Vashist, “Non-invasive glucose monitoring technology in diabetes management: a review,” Analytica Chimica Acta, vol. 750, pp. 16–27, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. G. Sparacino, M. Zanon, A. Facchinetti, C. Zecchin, A. Maran, and C. Cobelli, “Italian contributions to the development of continuous glucose monitoring sensors for diabetes management,” Sensors, vol. 12, no. 10, pp. 13753–13780, 2012. View at Publisher · View at Google Scholar
  28. K. V. Larin, M. S. Eledrisi, M. Motamedi, and R. O. Esenaliev, “Noninvasive blood glucose monitoring with optical coherence tomography: a pilot study in human subjects,” Diabetes Care, vol. 25, no. 12, pp. 2263–2267, 2002. View at Publisher · View at Google Scholar · View at Scopus
  29. M. A. Arnold and G. W. Small, “Noninvasive glucose sensing,” Analytical Chemistry, vol. 77, no. 17, pp. 5429–5439, 2005. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Caduff, M. S. Talary, M. Mueller et al., “Non-invasive glucose monitoring in patients with type 1 diabetes: a multisensor system combining sensors for dielectric and optical characterisation of skin,” Biosensors and Bioelectronics, vol. 24, no. 9, pp. 2778–2784, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. I. Harman-Boehm, A. Gal, A. M. Raykhman, E. Naidis, and Y. Mayzel, “Noninvasive glucose monitoring: increasing accuracy by combination of multi-technology and multi-sensors,” Journal of Diabetes Science and Technology, vol. 4, no. 3, pp. 583–595, 2010. View at Google Scholar · View at Scopus
  32. C. F. Amaral, M. Brischwein, and B. Wolf, “Multiparameter techniques for non-invasive measurement of blood glucose,” Sensors and Actuators B, vol. 140, no. 1, pp. 12–16, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. A. Caduff, M. Mueller, A. Megej et al., “Characteristics of a multisensor system for non invasive glucose monitoring with external validation and prospective evaluation,” Biosensors and Bioelectronics, vol. 26, no. 9, pp. 3794–3800, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. M. Mueller, M. S. Talary, L. Falco, O. de Feo, W. A. Stahel, and A. Caduff, “Data processing for noninvasive continuous glucose monitoring with a multisensor device,” Journal of Diabetes Science and Technology, vol. 5, no. 3, pp. 694–702, 2011. View at Google Scholar · View at Scopus
  35. M. Zanon, G. Sparacino, A. Facchinetti et al., “Non-invasive continuous glucose monitoring: improved accuracy of point and trend estimates of the multisensor system,” Medical and Biological Engineering and Computing, vol. 50, no. 10, pp. 1047–1057, 2012. View at Publisher · View at Google Scholar
  36. A. Caduff, M. S. Talary, and P. Zakharov, “Cutaneous blood perfusion as a perturbing factor for noninvasive glucose monitoring,” Diabetes Technology and Therapeutics, vol. 12, no. 1, pp. 1–9, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. P. Zakharov, F. Dewarrat, A. Caduff, and M. S. Talary, “The effect of blood content on the optical and dielectric skin properties,” Physiological Measurement, vol. 32, no. 1, pp. 131–149, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. D. Huber, M. Talary, F. Dewarrat, and A. Caduff, “The compensation of perturbing temperature fluctuation in glucose monitoring technologies based on impedance spectroscopy,” Medical and Biological Engineering and Computing, vol. 45, no. 9, pp. 863–876, 2007. View at Publisher · View at Google Scholar · View at Scopus
  39. P. Åberg, Skin cancer as seen by electrical impedance [Ph.D. thesis], Division of Medical Engineering, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden, 2004.
  40. S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Journal on Scientific Computing, vol. 20, no. 1, pp. 33–61, 1998. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  41. R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society B, vol. 58, no. 1, pp. 267–288, 1996. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  42. M. Schmidt, G. Fung, and R. Rosales, “Optimization methods for l1-regularization,” Tech. Rep. TR-2009-19, University of British Columbia, 2009. View at Google Scholar
  43. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, New York, NY, USA, 2004. View at MathSciNet
  44. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” Annals of Statistics, vol. 32, no. 2, pp. 407–499, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  45. T. Hastie, R. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, Springer, New York, NY, USA, 2nd edition, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  46. H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” Journal of the Royal Statistical Society B, vol. 67, no. 2, pp. 301–320, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  47. J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani, “Pathwise coordinate optimization,” The Annals of Applied Statistics, vol. 1, no. 2, pp. 302–332, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  48. D. L. Donoho and I. M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” Journal of the American Statistical Association, vol. 90, no. 432, pp. 1200–1224, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  49. A. J. van der Kooij, “Prediction accuracy and stability of regression with optimal scaling transformations,” Tech. Rep., Department of Data Theory, Leiden University, 2007. View at Google Scholar
  50. J. Friedman, T. Hastie, and R. Tibshirani, “Regularization paths for generalized linear models via coordinate descent,” Journal of Statistical Software, vol. 33, no. 1, pp. 1–22, 2010. View at Google Scholar · View at Scopus
  51. W. L. Clarke, D. Cox, L. A. Gonder-Frederick, W. Carter, and S. L. Pohl, “Evaluating clinical accuracy of systems for self-monitoring of blood glucose,” Diabetes Care, vol. 10, no. 5, pp. 622–628, 1987. View at Google Scholar · View at Scopus
  52. W. L. Clarke, S. Anderson, and B. Kovatchev, “Evaluating clinical accuracy of continuous glucose monitoring systems: continuous glucose—error grid analysis (CG-EGA),” Current Diabetes Reviews, vol. 4, no. 3, pp. 193–199, 2008. View at Publisher · View at Google Scholar · View at Scopus
  53. E. R. Damiano, F. H. El-Khatib, H. Zheng, D. M. Nathan, and S. J. Russell, “A comparative effectiveness analysis of three continuous glucose monitors,” Diabetes Care, vol. 36, no. 2, pp. 251–259, 2013. View at Publisher · View at Google Scholar
  54. A. M. K. Enejder, T. G. Scecina, J. Oh et al., “Raman spectroscopy for noninvasive glucose measurements,” Journal of Biomedical Optics, vol. 10, no. 3, Article ID 031114, 2005. View at Google Scholar · View at Scopus
  55. M. A. Arnold, L. Liu, and J. T. Olesberg, “Selectivity assessment of noninvasive glucose measurements based on analysis of multivariate calibration vectors,” Journal of Diabetes Science and Technology, vol. 1, no. 4, pp. 454–462, 2007. View at Google Scholar
  56. S. Guerra, G. Sparacino, A. Facchinetti, M. Schiavon, C. Dalla Man, and C. Cobelli, “A dynamic risk measure from continuous glucose monitoring data,” Diabetes Technology and Therapeutics, vol. 13, no. 8, pp. 843–852, 2011. View at Publisher · View at Google Scholar · View at Scopus