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 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.