Combining wavelet transform with convolutional neural networks for hypoglycemia events prediction from CGM data

Abstract

Estimating future blood glucose levels is an essential and challenging task for people with diabetes. It must be carried out based on variables such as current glucose, carbohydrate intake, physical activity, and insulin dosing. Accurate estimation is essential to maintain glucose values in a healthy range and avoid dangerous events of low glucose levels (hypoglycemia) and extremely high glucose values (hyperglycemia). Those situations maintained in time can cause not only permanent long-term damage but also short-term complications and even the death of the person. This paper proposes a new method to predict and detect hypoglycemic events over a 24-h time horizon. The technique combines applying the wavelet transform to glucose time series and deep learning convolutional neural networks. We have experimented with real data collected from 20 different people with type 1 diabetes. Our technique can also be applied to predict hyperglycemia. We incorporate a data augmentation technique consisting of a rolling windows system that improves the accuracy of the prediction. The uncertainty of the data is considered by the addition of controlled noise. The results show that the predictions obtained are accurate (higher than 88% of accuracy, sensitivity, specificity, and precision), confirming the effectiveness of the proposed method.