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dc.contributor.author | Laguna Sanz, Alejandro José | es_ES |
dc.contributor.author | Rossetti, Paolo | es_ES |
dc.contributor.author | Ampudia Blasco, F. Javier | es_ES |
dc.contributor.author | Vehí, Josep | es_ES |
dc.contributor.author | Bondía Company, Jorge | |
dc.date.accessioned | 2016-01-11T12:11:40Z | |
dc.date.available | 2016-01-11T12:11:40Z | |
dc.date.issued | 2014-07 | |
dc.identifier.issn | 1746-8094 | |
dc.identifier.uri | http://hdl.handle.net/10251/59647 | |
dc.description.abstract | [EN] Background Identification of individualized models for patients with type 1 diabetes is of vital importance for the development of a successful artificial pancreas and other model-based strategies of insulin treatment. However, the huge intra-patient glycemic variability frequently prevents the identification of reliable models, especially in the postprandial period. In this work, the identification of postprandial models characterizing intra-patient variability is addressed. Methods Regarding the postprandial response, uncertainties due to physiological variability, input errors in insulin infusion rate and in meal content estimation are characterized by means of interval models, which predict a glucose envelope containing all possible patient responses according to the model. Multi-objective optimization is performed over a cohort of virtual patients, minimizing both the fitting error and the output glucose envelope width. A Pareto Front is then built ranging from classic identification representing average behaviors to interval identification guaranteeing full enclosure of the measurements. A method for the selection of the best individual in the Pareto Front for identification from home monitoring data with a continuous glucose monitor is presented, reducing the overestimation of patient's variability due to monitor inaccuracies and noise. Results Identification using glucose reference data provide model bands that accurately fit all data points in the used virtual data set. Identification from continuous glucose monitor data, using two different width estimation procedures yield very similar prediction capabilities of around 60% of the data points predicted, and less than a 5% average error. Conclusions In this work, a new approach to evaluate intra-patient variability in the identification of postprandial models is presented. The proposed method is feasible and shows good prediction capabilities in a 5-h time horizon as compared to reference measurements. | es_ES |
dc.description.sponsorship | This work received funding from the Spanish Ministry of Science and Innovation under grant DPI2010-20764-C02, from the Generalitat Valenciana under project GV/2012/085, andfromthe European UnionSeventhFramework Programme (FP7/2007-2013) under grant agreement FP7-PEOPLE-2009-IEF, Ref 252085. The authors would like to acknowledge the collaboration of Sara Correa, Geles Viguer and Pepa Gabaldón of the Diabetes Reference Unit from the Clinic University Hospital of Valencia and, in particular, all the patients for participating in the study used in this work. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Biomedical Signal Processing and Control | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Parameter estimation | es_ES |
dc.subject | Physiological models | es_ES |
dc.subject | Biomedical systems | es_ES |
dc.subject | Medical applications | es_ES |
dc.subject | Uncertainty | es_ES |
dc.subject | Interval analysis | es_ES |
dc.subject.classification | INGENIERIA DE SISTEMAS Y AUTOMATICA | es_ES |
dc.title | Identification of intra-patient variability in the postprandial response of patients with type 1 diabetes | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.bspc.2013.07.003 | |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//DPI2010-20764-C02-02/ES/NUEVAS ESTRATEGIAS DE CONTROL GLUCEMICO POSTPRANDIAL MEDIANTE TERAPIA CON BOMBA DE INSULINA EN DIABETES TIPO 1/ | |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/FP7/252085/EU/Seeking solutions for the artificial pancreas: new methods for improving continuous glucose monitoring and closed-loop postprandial glycaemic control./ | |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//GV%2F2012%2F085/ | |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//DPI2010-20764-C02-01/ES/NUEVAS ESTRATEGIAS DE CONTROL GLUCEMICO POSTPRANDIAL MEDIANTE TERAPIA CON BOMBA DE INSULINA EN DIABETES TIPO 1/ | es_ES |
dc.rights.accessRights | Cerrado | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario de Automática e Informática Industrial - Institut Universitari d'Automàtica i Informàtica Industrial | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica | es_ES |
dc.description.bibliographicCitation | Laguna Sanz, AJ.; Rossetti, P.; Ampudia Blasco, FJ.; Vehí, J.; Bondía Company, J. (2014). Identification of intra-patient variability in the postprandial response of patients with type 1 diabetes. Biomedical Signal Processing and Control. 12:39-46. https://doi.org/10.1016/j.bspc.2013.07.003 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://doi.org/10.1016/j.bspc.2013.07.003 | |
dc.description.upvformatpinicio | 39 | es_ES |
dc.description.upvformatpfin | 46 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 12 | es_ES |
dc.relation.senia | 252126 | |
dc.contributor.funder | Ministerio de Ciencia e Innovación | |
dc.contributor.funder | Generalitat Valenciana | |
dc.contributor.funder | European Commission |