Bayesian analysis of blood glucose time series from diabetes home monitoring

Riccardo Bellazzi, Paolo Magni, Giuseppe De Nicolao

Research output: Contribution to journalArticlepeer-review

Abstract

This paper describes the application of a novel Bayesian estimation technique to extract the structural components, i.e., trend and daily patterns, from blood glucose level time series coming from home monitoring of insulin dependent diabetes mellitus patients. The problem is formulated through a set of stochastic equations, and is solved in a Bayesian framework by using a Markov chain Monte Carlo technique. The potential of the method is illustrated by analyzing data coming from the home monitoring of a 14-year old male patient.

Original languageEnglish
Pages (from-to)971-975
Number of pages5
JournalIEEE Transactions on Biomedical Engineering
Volume47
Issue number7
DOIs
Publication statusPublished - 2000

Keywords

  • Bayesian estimation
  • Insulin dependent diabetes mellitus
  • Markov chain Monte Carlo methods
  • Time series analysis

ASJC Scopus subject areas

  • Biomedical Engineering

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