Bayesian analysis of blood glucose time series from diabetes home monitoring

Author

Bellazzi, Riccardo ; Magni, Paolo ; De Nicolao, Giuseppe

Author_Institution

Dipt. di Inf. e Sistemistica, Pavia Univ., Italy

Volume

47

Issue

7

fYear

2000

fDate

7/1/2000 12:00:00 AM

Firstpage

971

Lastpage

975

Abstract

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.

Keywords

Bayes methods; Markov processes; Monte Carlo methods; biochemistry; blood; diseases; medical signal processing; organic compounds; patient monitoring; time series; 14 y; 14-year old male patient; Bayesian analysis; Markov chain Monte Carlo technique; blood glucose time series; diabetes home monitoring; insulin dependent diabetes mellitus patients; stochastic equations set; Bayesian methods; Blood; Diabetes; Equations; Insulin; Monte Carlo methods; Patient monitoring; Stochastic processes; Sugar; Time series analysis; Adolescent; Bayes Theorem; Biomedical Engineering; Blood Glucose Self-Monitoring; Diabetes Mellitus, Type 1; Humans; Male; Markov Chains; Monte Carlo Method; Stochastic Processes;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/10.846693

Filename

846693