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Short-term prediction of glucose in type 1 diabetes using kernel adaptive filters

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Abstract

This study aims at presenting a nonlinear, recursive, multivariate prediction model of the subcutaneous glucose concentration in type 1 diabetes. Nonlinear regression is performed in a reproducing kernel Hilbert space, by either the fixed budget quantized kernel least mean square (QKLMS-FB) or the approximate linear dependency kernel recursive least-squares (KRLS-ALD) algorithm, such that a sparse model structure is accomplished. A multivariate feature set (i.e., subcutaneous glucose, food carbohydrates, insulin regime and physical activity) is used and its influence on short-term glucose prediction is investigated. The method is evaluated using data from 15 patients with type 1 diabetes in free-living conditions. In the case when all the input variables are considered: (i) the average root mean squared error (RMSE) of QKLMS-FB increases from 13.1 mg dL−1 (mean absolute percentage error (MAPE) 6.6%) for a 15-min prediction horizon (PH) to 37.7 mg dL−1 (MAPE 20.8%) for a 60-min PH and (ii) the RMSE of KRLS-ALD, being predictably lower, increases from 10.5 mg dL−1 (MAPE 5.2%) for a 15-min PH to 31.8 mg dL−1 (MAPE 18.0%) for a 60-min PH. Multivariate data improve systematically both the regularity and the time lag of the predictions, reducing the errors in critical glucose value regions for a PH ≥ 30 min.

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Authors and Affiliations

  1. Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece

    Eleni I. Georga & Dimitrios I. Fotiadis

  2. Computational NeuroEngineering Laboratory, University of Florida, Gainesville, FL, USA

    José C. Príncipe

  3. Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology - Hellas (FORTH), Ioannina, Greece

    Dimitrios I. Fotiadis

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  1. Eleni I. Georga
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  2. José C. Príncipe
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Correspondence to Dimitrios I. Fotiadis.

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Georga, E.I., Príncipe, J.C. & Fotiadis, D.I. Short-term prediction of glucose in type 1 diabetes using kernel adaptive filters. Med Biol Eng Comput 57, 27–46 (2019). https://doi.org/10.1007/s11517-018-1859-3

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