NDLI: A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information

Content Provider	IEEE Xplore Digital Library
Author	Zecchin, C. Facchinetti, A. Sparacino, G. De Nicolao, G. Cobelli, C.
Copyright Year	2011
Description	Author affiliation: Department of Computer and System Science, University of Pavia, Pavia, Italy (De Nicolao, G.) \|\| Department of Information Engineering, University of Padova, Padova, Italy (Zecchin, C.; Facchinetti, A.; Sparacino, G.; Cobelli, C.)
Abstract	In the last decade, improvements in diabetes daily management have become possible thanks to the development of minimally-invasive portable sensors which allow continuous glucose monitoring (CGM) for several days. In particular, hypo and hyperglycemia can be promptly detected when glucose exceeds the normal range thresholds, and even avoided through the use of on-line glucose prediction algorithms. Several algorithms with prediction horizon (PH) of 15–30–45 min have been proposed in the literature, e.g. including AR/ARMA time-series modeling and neural networks. Most of them are fed by CGM signals only. The purpose of this work is to develop a new short-term glucose prediction algorithm based on a neural network that, in addition to past CGM readings, also exploits information on carbohydrates intakes quantitatively described through a physiological model. Results on simulated data quantitatively show that the new method outperforms other published algorithms. Qualitative preliminary results on a real diabetic subject confirm the potentialities of the new approach.
Starting Page	5653
Ending Page	5656
File Size	643802
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091368
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Sugar Diabetes Prediction algorithms Predictive models Monitoring Sensors
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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