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Glycemic Trend Prediction Using Empirical Model Identification

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Author

Summary, in English

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.

Publishing year

2009

Language

English

Pages

3501-3506

Publication/Series

Proc. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference (CDC2009 & CCC 2009)

Document type

Conference paper

Topic

  • Control Engineering

Keywords

  • subspace-based identification
  • biological systems

Conference name

Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference

Conference date

2009-12-16

Conference place

Shanghai, China

Status

Published

Project

  • DIAdvisor

Research group

  • LCCC