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dc.contributor.author | Prendin, Francesco | es_ES |
dc.contributor.author | Diez, José-Luís | es_ES |
dc.contributor.author | Del Favero, Simone | es_ES |
dc.contributor.author | Sparacino, Giovanni | es_ES |
dc.contributor.author | Facchinetti, Andrea | es_ES |
dc.contributor.author | Bondía Company, Jorge | es_ES |
dc.date.accessioned | 2023-11-27T19:00:40Z | |
dc.date.available | 2023-11-27T19:00:40Z | |
dc.date.issued | 2022-11 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/200254 | |
dc.description.abstract | [EN] Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model's prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH >= 45 and the NN-X for PH >= 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH >= 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min. | es_ES |
dc.description.sponsorship | This work was partially supported by the project "A noninvasive tattoo-based continuous GLUCOse Monitoring electronic system FOR Type-1 diabetes individuals (GLUCOMFORT)" (initiative "PRIN: Progetti di Rilevante Interesse Nazionale", call 2020, project ID: 2020X7XX2P) and through the initiative "Departments of Excellence" (Law 232/2016); Grant DPI2016-78831-C2-1-R funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe", Grant PID2019-107722RB-C21 funded by MCIN/AEI/10.13039/501100011033, and the mobility Grant PRX19/00463 funded by MCIN/AEI/10.13039/501100011033 for a research stay of J. Bondia at the University of Padova. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Sensors | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Type 1 diabetes | es_ES |
dc.subject | Glucose prediction | es_ES |
dc.subject | Fuzzy clustering | es_ES |
dc.subject | Seasonal local models | es_ES |
dc.subject.classification | INGENIERIA DE SISTEMAS Y AUTOMATICA | es_ES |
dc.title | Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/s22228682 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107722RB-C21/ES/SOLUCIONES A MEDIDA DEL PACIENTE PARA EL CONTROL DE GLUCOSA EN SANGRE EN DIABETES TIPO 1/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//DPI2016-78831-C2-1-R//SOLUCIONES PARA LA MEJORA DE LA EFICIENCIA Y SEGURIDAD DEL PÁNCREAS ARTIFICIAL MEDIANTE ARQUITECTURAS DE CONTROL MULTIVARIABLE TOLERANTES A FALLOS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//PRX19%2F00463/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MUR//2020X7XX2P//A noninvasive tattoo-based continuous GLUCOse Monitoring electronic system FOR Type-1 diabetes individuals (GLUCOMFORT)/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials | es_ES |
dc.description.bibliographicCitation | Prendin, F.; Diez, J.; Del Favero, S.; Sparacino, G.; Facchinetti, A.; Bondía Company, J. (2022). Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions. Sensors. 22(22):1-17. https://doi.org/10.3390/s22228682 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/s22228682 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 17 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 22 | es_ES |
dc.description.issue | 22 | es_ES |
dc.identifier.eissn | 1424-8220 | es_ES |
dc.identifier.pmid | 36433278 | es_ES |
dc.identifier.pmcid | PMC9694694 | es_ES |
dc.relation.pasarela | S\488362 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | Università degli studi di Padova | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Ministero dell'Università e della Ricerca | es_ES |
dc.subject.ods | 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades | es_ES |