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ORCID: https://orcid.org/0000-0003-1551-4613, Hernández González, Liss, Cancela González, Jorge, Isabel, Miguel María, Facchinetti, Andrea, Fabris, Chiara, Gabriel, Rafael, Cobelli, Claudio and Arredondo Waldmeyer, María Teresa
ORCID: https://orcid.org/0000-0003-3113-3976
(2017).
Exploring the frequency domain of continuous glucose monitoring signals to improve characterization of glucose variability and of diabetic profiles.
"Journal of Diabetes Science and Technology", v. 11
(n. 4);
pp. 773-779.
https://doi.org/10.1177/1932296816685717.
| Título: | Exploring the frequency domain of continuous glucose monitoring signals to improve characterization of glucose variability and of diabetic profiles |
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| Autor/es: |
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| Tipo de Documento: | Artículo |
| Título de Revista/Publicación: | Journal of Diabetes Science and Technology |
| Fecha: | 2017 |
| Volumen: | 11 |
| Número: | 4 |
| Materias: | |
| ODS: | |
| Escuela: | E.T.S.I. Telecomunicación (UPM) |
| Departamento: | Tecnología Fotónica y Bioingeniería |
| Licencias Creative Commons: | Ninguna |
|
PDF (Portable Document Format)
- Acceso permitido solamente al administrador del Archivo Digital UPM
- Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (315kB) |
Background:
Continuous glucose monitoring (CGM) devices measure interstitial glucose concentrations (normally every 5 minutes), allowing observation of glucose variability (GV) patterns during the whole day. This information could be used to improve prescription of treatments and of insulin dosages for people suffering diabetes. Previous efforts have been focused on proposing indices of GV either in time or glucose domains, while the frequency domain has been explored only partially. The aim of this work is to explore the CGM signal in the frequency domain to understand if new indexes or features could be identified and contribute to a better characterization of glucose variability.
Methods:
The direct fast Fourier transform (FFT) and the Welch method were used to analyze CGM signals from three different profiles: people at risk of developing type 2 diabetes (P@R), T2D patients, and type 1 diabetes (T1D) patients.
Results:
The results suggests that features extracted from the FFT (ie, the localization and power of the maximum peak of the power spectrum and the bandwidth at 3 dB) are able to provide a characterization for all the three populations under study compared with the Welch approach.
Conclusions:
Such preliminary results can represent a good insight for futures investigations with the possibility of building and using new indexes of glucose variability based on the frequency features.
| ID de Registro: | 87352 |
|---|---|
| Identificador DC: | https://oa.upm.es/87352/ |
| Identificador OAI: | oai:oa.upm.es:87352 |
| URL Portal Científico: | https://portalcientifico.upm.es/es/ipublic/item/5643178 |
| Identificador DOI: | 10.1177/1932296816685717 |
| URL Oficial: | https://journals.sagepub.com/doi/10.1177/193229681... |
| Depositado por: | Dr. Giuseppe Fico |
| Depositado el: | 29 Ene 2025 16:17 |
| Ultima Modificación: | 12 Mar 2025 12:51 |
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