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Anal Chim Acta. 2019 Apr 4;1052:37-48. doi: 10.1016/j.aca.2018.12.009. Epub 2018 Dec 15.

Non-invasive prediction of blood glucose trends during hypoglycemia.

Author information

1
Department of Clinical and Biomedical Engineering, Oslo University Hospital, Oslo, Norway. Electronic address: chrton@ous-hf.no.
2
Department of Clinical and Biomedical Engineering, Oslo University Hospital, Oslo, Norway.
3
Prediktor Medical AS, Fredrikstad, Norway; Norwegian University of Science and Technology, Department of Engineering Cybernetics, Trondheim, Norway.
4
Department of Clinical and Biomedical Engineering, Oslo University Hospital, Oslo, Norway; Department of Physics, University of Oslo, Oslo, Norway.
5
Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway; Metabolic and Renal Research Group, UiT The Arctic University of Norway, Tromsø, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
6
Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Abstract

Over the last four decades, there has been a pursuit for a non-invasive solution for glucose measurement, but there is not yet any viable product released. Of the many sensor modalities tried, the combination of electrical and optical measurement is among the most promising for continuous measurements. Although non-invasive prediction of exact glucose levels may seem futile, prediction of their trends may be useful for certain applications. Hypoglycemia is the most serious of the acute complications in type-1 diabetes highlighting the need for a reliable alarm, but little is known about the performance of this technology in predicting hypoglycemic glucose levels and associated trends. We aimed to assess such performance on the way to develop a multisensor system for detection of hypoglycemia, based on near-infrared (NIR), bioimpedance and skin temperature measurements taken during hypoglycemic and euglycemic glucose clamps in 20 subjects with type-1 diabetes. Performance of blood glucose prediction was assessed by global partial least squares and neural network regression models using repeated double cross-validation. Best trend prediction was obtained by including all measurements in a neural network model. Prediction of glucose level was inaccurate for threshold-based detection of hypoglycemia, but the trend predictions may provide useful information in a multisensor system. Comparing NIR and bioimpedance measurements, NIR seems to be the main predictor of blood glucose while bioimpedance may act as correction for individual confounding properties.

KEYWORDS:

Glucose; Hypoglycemia; Machine learning; Multisensor; Neural network; Non invasive

PMID:
30685040
DOI:
10.1016/j.aca.2018.12.009
[Indexed for MEDLINE]

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