用血糖动态特征在线识别进餐事件，提升准确率与可解释性。
Online Meal Detection Based on CGM Data Dynamics
- 从连续血糖数据中提取动态模式作为特征
- 在多个数据集上实现更高检测准确率
- 适合糖尿病管理与实时健康监测场景
我们利用连续血糖监测（CGM）数据中的动态模式作为特征，用于检测进餐事件。通过挖掘底层动力学的固有特性，这些动态模式能够捕捉葡萄糖波动的关键特征，从而识别与进食相关的模式和异常。该方法不仅提升了进餐检测的准确性，还增强了对血糖动力学的可解释性。通过聚焦动态特征，本方法构建了稳健的特征提取框架，实现了跨不同数据集的良好泛化能力，并确保在真实应用场景中的可靠性能。相比传统方法，该技术显著提升了检测精度。
原文摘要 · Abstract (English)
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,
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