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

Harald Kirchsteiger, Rolf Johansson, Eric Renard, Luigi Del Re

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1454-1466
Number of pages13
JournalInternational Journal of Control
Issue number7
Publication statusPublished - 3 Jul 2014
Externally publishedYes


  • Biomedical systems
  • Identification
  • Interval model
  • Type 1 diabetes


Dive into the research topics of 'Continuous-time interval model identification of blood glucose dynamics for type 1 diabetes'. Together they form a unique fingerprint.

Cite this