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Journal of Electrical and Computer Engineering
Volume 2011, Article ID 681786, 11 pages
http://dx.doi.org/10.1155/2011/681786
Research Article

Blood Glucose Prediction Using Artificial Neural Networks Trained with the AIDA Diabetes Simulator: A Proof-of-Concept Pilot Study

1Bioengineering Department, University of Strathclyde, Wolfson Building, 106 Rottenrow, Glasgow G4 0NW, UK
2Department of Imaging, CMRU, Imperial College of Science, Technology and Medicine, Royal Brompton Hospital, Sydney Street, London SW3 6NP, UK
3Scotsig, 40 Westbourne Gardens, Glasgow G12 9PF, UK
4Ateeda Limited, CBC House, 24 Canning Street, Edinburgh EH3 8EG, UK

Received 3 November 2010; Accepted 18 February 2011

Academic Editor: John Walsh

Copyright © 2011 Gavin Robertson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Diabetes mellitus is a major, and increasing, global problem. However, it has been shown that, through good management of blood glucose levels (BGLs), the associated and costly complications can be reduced significantly. In this pilot study, Elman recurrent artificial neural networks (ANNs) were used to make BGL predictions based on a history of BGLs, meal intake, and insulin injections. Twenty-eight datasets (from a single case scenario) were compiled from the freeware mathematical diabetes simulator, AIDA. It was found that the most accurate predictions were made during the nocturnal period of the 24 hour daily cycle. The accuracy of the nocturnal predictions was measured as the root mean square error over five test days () not used during ANN training. For BGL predictions of up to 1 hour a of (±SD)  mmol/L was observed. For BGL predictions up to 10 hours, a of (±SD)  mmol/L was observed. Future research will investigate a wider range of AIDA case scenarios, real-patient data, and data relating to other factors influencing BGLs. ANN paradigms based on real-time recurrent learning will also be explored to accommodate dynamic physiology in diabetes.