Classification of postprandial glycemic patterns in type 1 diabetes subjects under closed-loop control: an in silico study

In this contribution we explore some alternatives in order to obtain filtered and low dimension CGM data to provide well processed CGM data to AP systems. The presented approach explores the possible association of certain patient behaviors with certain glucose patterns. We compare the classical clu...

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Published inConference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Vol. 2019; pp. 5443 - 5446
Main Authors Schroder, Corinna, Diez, Jose Luis, Laguna, Alejandro J., Bondia, Jorge, Tarin, Cristina
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2019
Subjects
Absorption
Clustering algorithms
Ellipsoids
Insulin
Sugar
Time series analysis
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ISSN1557-170X
1558-4615
DOI10.1109/EMBC.2019.8857246

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Summary:In this contribution we explore some alternatives in order to obtain filtered and low dimension CGM data to provide well processed CGM data to AP systems. The presented approach explores the possible association of certain patient behaviors with certain glucose patterns. We compare the classical clustering algorithms (K-means, and fuzzy C-means), which has shown some limitations for CGM data processing, with a new clustering algorithm (K-means ellipsoid algorithm) more suited to CGM data. We test this new algorithm in a variety of complex scenarios including variabilty in the amount of ingested carbohydrates, absorption time and intrapatient parameters. The new algorithm overcomes the perceived problems and is able to discriminate between normoglycaemic, moderate and severe hyperglycaemic post-prandial behaviour, even with similar amounts of carbohydrates contained in a meal.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2019.8857246