Imperial College London
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Professor Pantelis Georgiou

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Biomedical Electronics
 
 
 
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Contact

 

+44 (0)20 7594 6326pantelis Website

 
 
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Location

 

902Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Li:2019:10.1109/JBHI.2019.2931842,
author = {Li, K and Liu, C and Zhu, T and Herrero, P and Georgiou, P},
doi = {10.1109/JBHI.2019.2931842},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {414--423},
title = {GluNet: A deep learning framework for accurate glucose forecasting.},
url = {http://dx.doi.org/10.1109/JBHI.2019.2931842},
volume = {24},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - For people with Type 1 diabetes (T1D), forecasting of \red{blood glucose (BG)} can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to observe glucose in real-time. However, an accurate glucose forecast remains a challenge. In this work, we introduce GluNet, a framework that leverages on a personalized deep neural network to predict the probabilistic distribution of short-term (30-60 minutes) future CGM measurements for subjects with T1D based on their historical data including glucose measurements, meal information, insulin doses, and other factors. It adopts the latest deep learning techniques consisting of four components: data pre-processing, label transform/recover, multi-layers of dilated convolution neural network (CNN), and post-processing. The method is evaluated in-silico for both adult and adolescent subjects. The results show significant improvements over existing methods in the literature through a comprehensive comparison in terms of root mean square error (RMSE) (8.88 ± 0.77 mg/dL) with short time lag (0.83 ± 0.40 minutes) for prediction horizons (PH) = 30 mins (minutes), and RMSE (19.90 ± 3.17 mg/dL) with time lag (16.43 ± 4.07 mins) for PH = 60 mins for virtual adult subjects. In addition, GluNet is also tested on two clinical data sets. Results show that it achieves an RMSE (19.28 ± 2.76 mg/dL) with time lag (8.03 ± 4.07 mins) for PH = 30 mins and an RMSE (31.83 ± 3.49 mg/dL) with time lag (17.78 ± 8.00 mins) for PH = 60 mins. These are the best reported results for glucose forecasting when compared with other methods including the neural network for predicting glucose (NNPG), the support vector regression (SVR), the latent variable with exogenous input (LVX), and the auto regression with exogenous input (ARX) algorithm.
AU  - Li,K
AU  - Liu,C
AU  - Zhu,T
AU  - Herrero,P
AU  - Georgiou,P
DO  - 10.1109/JBHI.2019.2931842
EP  - 423
PY  - 2019///
SN  - 2168-2194
SP  - 414
TI  - GluNet: A deep learning framework for accurate glucose forecasting.
T2  - IEEE Journal of Biomedical and Health Informatics
UR  - http://dx.doi.org/10.1109/JBHI.2019.2931842
UR  - https://www.ncbi.nlm.nih.gov/pubmed/31369390
UR  - https://ieeexplore.ieee.org/document/8779644
UR  - http://hdl.handle.net/10044/1/72581
VL  - 24
ER  -
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