An LSTM-based Approach Towards Automated Meal Detection from Continuous Glucose Monitoring in Type 1 Diabetes Mellitus

Technological advancements in glucose sensing, insulin pumps, and closed-loop glucose control algorithms open new opportunities towards the realization of Artificial Pancreas (AP). However, the effective management of meal disturbances in these systems still remains a challenge. Meal detection algorithms eliminate the need for meal announcements and enable the shift to more automated and reliable AP systems. The aim of the present study is to develop and evaluate a personalized approach for the detection of meal disturbances in patients with Type 1 Diabetes Mellitus (T1DM). Long Short Term Memory Neural Networks (LSTM)'s inherent ability to efficiently handle sequential data is leveraged within an ensemble learning strategy towards the development of different versions of ensemble models. The models receive as input sequences of Continuous Glucose Monitoring (CGM) measurements (glucose profiles) of a 120-min duration and classify them as positive or negative for the onset of an ingested meal. In silico evaluation is performed using the UVA-PADOVA T1DM Simulator. All ensembles achieve acceptable discriminative performance (mean c-statistic: 75.12%-79.52%) and are able to detect meals in a timely manner (mean detection time: 7.08-12.84 min). Statistical analysis demonstrates the superiority of the simple averaging combination scheme over the other schemes in terms of the c-statistic.