Document Type

Article

Research Focus Area

Health Care Technology and Biomedical Engineering

Publication Date

2013

Journal or Book Title

Industrial and Engineering Chemistry Research

Volume

52

Issue

35

First Page

12329

Last Page

12336

DOI

10.1021/ie3034015

Abstract

Hypoglycemia is a major challenge of artificial pancreas systems and a source of concern for potential users and parents of young children with Type 1 diabetes (T1D). Early alarms to warn of the potential of hypoglycemia are essential and should provide enough time to take action to avoid hypoglycemia. Many alarm systems proposed in the literature are based on interpretation of recent trends in glucose values. In the present study, subject-specific recursive linear time series models are introduced as a better alternative to capture glucose variations and predict future blood glucose concentrations. These models are then used in hypoglycemia early alarm systems that notify patients to take action to prevent hypoglycemia before it happens. The models developed and the hypoglycemia alarm system are tested retrospectively using T1D subject data. A Savitzky-Golay filter and a Kalman filter are used to reduce noise in patient data. The hypoglycemia alarm algorithm is developed by using predictions of future glucose concentrations from recursive models. The modeling algorithm enables the dynamic adaptation of models to inter/intra-subject variation and glycemic disturbances and provides satisfactory glucose concentration prediction with relatively small error. The alarm systems demonstrate good performance in prediction of hypoglycemia and ultimately in prevention of its occurrence.

Comments

Reprinted (adapted) with permission from Industrial and Engineering Chemistry Research 52 (2013): 12329, doi: 10.1021/ie3034015. Copyright 2013 American Chemical Society.

Copyright Owner

American Chemical Society

Language

en

File Format

application/pdf

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