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Glycemic Trend Prediction Using Empirical Model Identification

Publiceringsår: 2009
Språk: Engelska
Sidor: 3501-3506
Dokumenttyp: Konferensbidrag


Using methods of system identification and prediction, we investigate near-future prediction of individual specific T1DM blood glucose dynamics with the purpose of a decision-making tool development in diabetes treatment. Two strategies were approached: Firstly, Kalman estimators based on identified state-space models were designed; Secondly, direct identification of ARX- and ARMAX-based predictors was done.
Predictions over 30 minutes look-ahead were capable to track
glucose variation even in sensible ranges for estimation data,
but not on validation data.



  • Technology and Engineering
  • subspace-based identification
  • biological systems


Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference
Shanghai, China
  • LCCC

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