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Med Biol Eng Comput. 2016 Oct;54(10):1563-77. doi: 10.1007/s11517-015-1436-y. Epub 2015 Dec 30.

A simplification of Cobelli's glucose-insulin model for type 1 diabetes mellitus and its FPGA implementation.

Li P1,2,3, Yu L4,5, Fang Q6, Lee SY7.

Author information

1
Department of Medical Electronics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China. lipeng@sibet.ac.cn.
2
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China. lipeng@sibet.ac.cn.
3
University of Chinese Academy of Sciences, Beijing, China. lipeng@sibet.ac.cn.
4
Department of Medical Electronics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
5
University of Chinese Academy of Sciences, Beijing, China.
6
School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia.
7
Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.

Abstract

Cobelli's glucose-insulin model is the only computer simulator of glucose-insulin interactions accepted by Food Drug Administration as a substitute to animal trials. However, it consists of multiple differential equations that make it hard to be implemented on a hardware platform. In this investigation, the Cobelli's model is simplified by Padé approximant method and implemented on a field-programmable gate array-based platform as a hardware model for predicting glucose changes in subjects with type 1 diabetes mellitus. Compared with the original Cobelli's model, the implemented hardware model provides a nearly perfect approximation in predicting glucose changes with rather small root-mean-square errors and maximum errors. The RMSE results for 30 subjects show that the method for simplifying and implementing Cobelli's model has good robustness and applicability. The successful hardware implementation of Cobelli's model will promote a wider adoption of this model that can substitute animal trials, provide fast and reliable glucose and insulin estimation, and ultimately assist the further development of an artificial pancreas system.

KEYWORDS:

Artificial pancreas system; FPGA; Glucose–insulin interaction model; Model order reduction; T1DM

PMID:
26718555
DOI:
10.1007/s11517-015-1436-y
[Indexed for MEDLINE]

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