Continuous-time interval model identification of blood glucose dynamics for type 1 diabetes

Abstract

While good physiological models of the glucose metabolism in type 1 diabetic patients are well known, their parameterisation is difficult. The high intra-patient variability observed is a further major obstacle. This holds for data-based models too, so that no good patient-specific models are available. Against this background, this paper proposes the use of interval models to cover the different metabolic conditions. The control-oriented models contain a carbohydrate and insulin sensitivity factor to be used for insulin bolus calculators directly. Available clinical measurements were sampled on an irregular schedule which prompts the use of continuous-time identification, also for the direct estimation of the clinically interpretable factors mentioned above. An identification method is derived and applied to real data from 28 diabetic patients. Model estimation was done on a clinical data-set, whereas validation results shown were done on an out-of-clinic, everyday life data-set. The results show that the interval model approach allows a much more regular estimation of the parameters and avoids physiologically incompatible parameter estimates.

Keywords:

• identification
• biomedical systems
• type 1 diabetes
• interval model

Acknowledgements

The authors gratefully acknowledge the sponsoring of this work by the COMET K2 centre ‘Austrian Center of Competence in Mechatronics (ACCM)’. The COMET programme is funded by the Austrian federal government, the federal state Upper Austria and the scientific partners of ACCM. The second author is a member of the LCCC Linnaeus Center and the eLLIIT Excellence Center at Lund University.