Other Open Access

GLUCOSE FORECASTING USING A PHYSIOLOGICAL MODEL AND STATE ESTIMATION

Chengyuan, Liu, Panteliu Georgiou, Pau Herrero; Oliver, Nick

Accurate glucose forecasting algorithms have been proven to be an effective solution for reducing the risk of hypo- and hyperglycaemia events when combined with glucose alarms and/or low-insulin suspension systems. • Effective glucose forecasting algorithm can be applied to control insulin delivery in automatic systems. • Daily routine information (e.g. meal intake, insulin injection, physical exercises) of patient can be included into forecasting algorithms to improve prediction accuracy. • In this work, we introduce a novel model-based glucose prediction algorithm which uses deconvolution of the continuous glucose monitoring (CGM) signal to estimate some of the model states in order to improve prediction accuracy.

Files (2.0 MB)
Name Size
ATTD2018.pdf
md5:f0749f0e24c54d94826ed462261b23ea
2.0 MB Download
14
10
views
downloads
All versions This version
Views 1414
Downloads 1010
Data volume 20.2 MB20.2 MB
Unique views 1212
Unique downloads 88

Share

Cite as