A stochastic model to assess the variability of blood glucose time series in diabetic patients self-monitoring

Paolo Magni, Riccardo Bellazzi

Research output: Contribution to journalArticlepeer-review

Abstract

Several studies have shown that patients suffering from Diabetes Mellitus can significantly delay the onset and slow down the progression of diabetes micro- and macro-angiopathic complications through intensive monitoring and treatment. In general, intensive treatments imply a careful blood glucose level (BGL) self-monitoring. The analysis of BGL measurements is one of the most important tasks in order to assess the glucose metabolic control and to revise the therapeutic protocol. Recent clinical studies have shown the correlation between the glucose variability and the long-term diabetes related complications. In this paper, we propose a stochastic model to extract the time course of such variability from the self-monitoring BGL time series. This information can be conveniently combined with other analysis to evaluate the adequacy of the therapeutic protocol and to highlight periods characterized by an increasing glucose instability. The method here proposed has been validated on two simulated data sets and tested with success in the retrospective analysis of three patients' data sets.

Original languageEnglish
Article number1634491
Pages (from-to)977-985
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume53
Issue number6
DOIs
Publication statusPublished - Jun 2006

Keywords

  • Bayesian estimation
  • Blood glucose level time series analysis
  • Blood glucose variability
  • Diabetes long-term complications
  • Diabetic patients home monitoring
  • Markov chain Monte Carlo methods
  • Risk index
  • Stochastic volatility models

ASJC Scopus subject areas

  • Biomedical Engineering

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