Vous êtes ici : Accueil > Personalization of a compartmental physiological model for an artificial pancreas through integration of patient's state estimation

Publications

Personalization of a compartmental physiological model for an artificial pancreas through integration of patient's state estimation

Publié le 29 mars 2018
Personalization of a compartmental physiological model for an artificial pancreas through integration of patient's state estimation
Auteurs
Jallon P., Lachal S., Franco C., Charpentier G., Huneker E., Doron M.
Year2017-0442
Source-TitleProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Affiliations
Univ. Grenoble Alpes, France, CEA, LETI, MINATEC Campus, Grenoble, France, Centre Hospitalier Sud-Francilien, Department of Diabetes and Endocrinology, Corbeil-Essones, France, Centre d'Etudes et de Recherche Pour l'Intensification du Traitement du Diabète (CERITD), Corbeil-Essonnes, France, Diabeloop SAS, 155 Cours Berriat, France
Abstract
Artificial Pancreas (AP) are developed for patients with Type 1 diabetes. This medical device system consists in the association of a subcutaneous continuous glucose monitor (CGM) providing a proxy of the patient's glycaemia and a control algorithm offering the real-time modification of the insulin delivery with an automatic command of the subcutaneous insulin pump. The most complex algorithms are based on a compartmental model of the glucoregulatory system of the patient coupled to an approach of MPC (Model-Predictive-Control) for the command. The automatic and unsupervised control of insulin regulation constitutes a major challenge in AP projects. A given model with its parameterization on the shelf will not directly represent the patient's data behavior and the personalization of the model is a prerequisite before using it in a MPC. The present paper focuses on the personalization of a compartmental showing a method where taking into account the estimation of the patient's state in addition to the parameter estimation improves the results in terms of mean quadratic error. © 2017 IEEE.
Author-Keywords
 
Index-Keywords
 
ISSN1557170X
Lien vers articleLink

Retour à la liste