Detection of glycemic excursions using morphological and time-domain ECG features

Managing diabetes often involves monitoring blood glucose in real time to detect excursions (e.g., hypoglycemia and hyperglycemia). Continuous glucose monitors (CGMs) are generally used for this purpose, but CGMs are both expensive and invasive (they require inserting a flexible needle under the skin). To address this issue, we examine whether non-invasive devices, such as electrocardiograms (ECG), can be used to predict glucose excursions. In particular, we consider two types of cardiac information: (1) heartbeat morphology, which generally requires ECG recordings, and (2) heartbeat timing, which can be obtained from inexpensive wrist-worn devices, such as fitness trackers. We use convolutional networks to analyze beat morphology, and recurrent networks and feature engineering to analyze the inter-beat interval (IBI) time series. Then, we validate individual models and their combinations on an experimental dataset containing ECG and CGM recordings for then young adults with type 1 diabetes. We find that beat morphology outperforms beat timing in hypoglycemia prediction, but the reverse happens for hyperglycemia prediction. In both prediction problems, combining morphology and time-domain information outperforms using each source of information independently.