Literature DB >> 23428611

Safety auxiliary feedback element for the artificial pancreas in type 1 diabetes.

A Revert1, F Garelli, J Pico, H De Battista, P Rossetti, J Vehi, J Bondia.   

Abstract

The artificial pancreas aims at the automatic delivery of insulin for glycemic control in patients with type 1 diabetes, i.e., closed-loop glucose control. One of the challenges of the artificial pancreas is to avoid controller overreaction leading to hypoglycemia, especially in the late postprandial period. In this study, an original proposal based on sliding mode reference conditioning ideas is presented as a way to reduce hypoglycemia events induced by a closed-loop glucose controller. The method is inspired in the intuitive advantages of two-step constrained control algorithms. It acts on the glucose reference sent to the main controller shaping it so as to avoid violating given constraints on the insulin-on-board. Some distinctive features of the proposed strategy are that 1) it provides a safety layer which can be adjusted according to medical criteria; 2) it can be added to closed-loop controllers of any nature; 3) it is robust against sensor failures and overestimated prandial insulin doses; and 4) it can handle nonlinear models. The method is evaluated in silico with the ten adult patients available in the FDA-accepted UVA simulator.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 23428611     DOI: 10.1109/TBME.2013.2247602

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  10 in total

1.  Parallel Control of an Artificial Pancreas with Coordinated Insulin, Glucagon, and Rescue Carbohydrate Control Actions.

Authors:  Vanessa Moscardó; José Luis Díez; Jorge Bondia
Journal:  J Diabetes Sci Technol       Date:  2019-10-20

Review 2.  Fault detection and safety in closed-loop artificial pancreas systems.

Authors:  B Wayne Bequette
Journal:  J Diabetes Sci Technol       Date:  2014-07-21

3.  Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems.

Authors:  Iman Hajizadeh; Mudassir Rashid; Sediqeh Samadi; Jianyuan Feng; Mert Sevil; Nicole Hobbs; Caterina Lazaro; Zacharie Maloney; Rachel Brandt; Xia Yu; Kamuran Turksoy; Elizabeth Littlejohn; Eda Cengiz; Ali Cinar
Journal:  J Diabetes Sci Technol       Date:  2018-03-23

4.  Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement.

Authors:  Emilia Fushimi; Patricio Colmegna; Hernán De Battista; Fabricio Garelli; Ricardo Sánchez-Peña
Journal:  J Diabetes Sci Technol       Date:  2019-07-24

5.  Postprandial fuzzy adaptive strategy for a hybrid proportional derivative controller for the artificial pancreas.

Authors:  Aleix Beneyto; Josep Vehi
Journal:  Med Biol Eng Comput       Date:  2018-05-03       Impact factor: 2.602

Review 6.  A Review of Safety and Design Requirements of the Artificial Pancreas.

Authors:  Helga Blauw; Patrick Keith-Hynes; Robin Koops; J Hans DeVries
Journal:  Ann Biomed Eng       Date:  2016-06-28       Impact factor: 3.934

7.  Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor.

Authors:  Lyvia Biagi; Charrise M Ramkissoon; Andrea Facchinetti; Yenny Leal; Josep Vehi
Journal:  Sensors (Basel)       Date:  2017-06-12       Impact factor: 3.576

8.  Potentials of constrained sliding mode control as an intervention guide to manage COVID19 spread.

Authors:  Sebastián Nuñez; Fernando A Inthamoussou; Fernando Valenciaga; Hernán De Battista; Fabricio Garelli
Journal:  Biomed Signal Process Control       Date:  2021-03-10       Impact factor: 3.880

9.  Dilated Recurrent Neural Networks for Glucose Forecasting in Type 1 Diabetes.

Authors:  Taiyu Zhu; Kezhi Li; Jianwei Chen; Pau Herrero; Pantelis Georgiou
Journal:  J Healthc Inform Res       Date:  2020-04-12

10.  Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System.

Authors:  Arthur Bertachi; Lyvia Biagi; Aleix Beneyto; Josep Vehí
Journal:  J Healthc Eng       Date:  2020-01-11       Impact factor: 2.682

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.