Online Meal Detection Based on CGM Data Dynamics
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915366282723328 |
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| author | Tavasoli, Ali Shakeri, Heman |
| author_facet | Tavasoli, Ali Shakeri, Heman |
| contents | 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, |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00080 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Meal Detection Based on CGM Data Dynamics Tavasoli, Ali Shakeri, Heman Machine Learning Adaptation and Self-Organizing Systems Applications 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, |
| title | Online Meal Detection Based on CGM Data Dynamics |
| topic | Machine Learning Adaptation and Self-Organizing Systems Applications |
| url | https://arxiv.org/abs/2507.00080 |