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People with diabetes have to manage their blood glucose level to keep itwithin an appropriate range. Predicting whether future glucose values will beoutside the healthy threshold is of vital importance in order to takecorrective actions to avoid potential health damage. In this paper we describeour research with the aim of predicting the future behavior of blood glucoselevels, so that hypoglycemic events may be anticipated. The approach of thiswork is the application of transformation functions on glucose time series, andtheir use in convolutional neural networks. We have tested our proposed methodusing real data from 4 different diabetes patients with promising results.

Patterns Detection in Glucose Time Series by Domain Transformations and Deep Learning