Online Meal Detection Based on CGM Data Dynamics

Heman Shakeri, Ali Tavasoli

Abstract:

We utilize dynamical modes as features derived from Continuous Glucose Monitoring
(CGM) data to detect meal events. By leveraging the inherent properties of underlying dynamics,
these modes capture key aspects of glucose variability, enabling the identification of patterns and
anomalies associated with meal consumption. This approach not only improves the accuracy of
meal detection but also enhances the interpretability of the underlying glucose dynamics. By
focusing on dynamical features, our method provides a robust framework for feature extraction,
facilitating generalization across diverse datasets and ensuring reliable performance in real-world
applications. The proposed technique offers significant advantages over traditional approaches,
improving detection accuracy, detection delay, and system robustness.