Automatic event detection oncontinuous glucose datausing neural networks

Abstract: Automatically detecting events for people with diabetes mellitus using continuousglucose monitors is an important step in allowing insulin pumps to automaticallycorrect the blood glucose levels and for a more hands-off approach to thedisease. The automatic detection of events could also aid physicians whenassisting their patients when referring to their continuous glucose monitordata. A range of different deep learning algorithms has been applied forpredictions of different events for continuous glucose monitor data, such asthe onset for hyperglycemia, hypoglycemia or mealtime events. This thesisfocused on constructing sequences labelled from an unbalanced and assumedmisslabelled dataset to classify them as such using four different deep learningnetworks using convoluted neural networks and recurrent neural networks.Manual correction of the dataset allowed for only clear events starting witha high positive gradient to be labelled as positive. The classification wasperformed on exact timepoints and in time windows to allow the classificationto to be done around the beginning of an event instead of the exact timepoint.The results from using the unbalanced and assumed misslabelled datasetshowed the networks performing similarly, with high Recall and Precisionbelow 0.5, thus not found to be of use in a for automatic event detection.Further testing by using another dataset or further configurations is neededto clarify the capabilities of automatically detecting events. DDAnalytics willnot use any of the developed networks in any of their products.