Online Meal Detection Based on CGM Data Dynamics

TL;DR

This paper introduces a novel method for detecting meals using dynamical features derived from CGM data, improving accuracy and interpretability in glucose monitoring applications.

Contribution

It presents a new approach leveraging dynamical modes from CGM data for more accurate and interpretable meal detection.

Findings

Enhanced detection accuracy over traditional methods

Robustness across diverse datasets

Improved interpretability of glucose dynamics

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,