Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques

The ultimate goal of an artificial pancreas is finding the optimal insulin rates that can effectively reduce high blood glucose (BG) levels in type 1 diabetic patients. To achieve this, most closed-loop control strategies need to compute the optimal insulin action on the basis of precedent glucose a...

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Detalles Bibliográficos
Autores: Avila, Luis Omar, de Paula, Mariano, Sánchez Reinoso, Carlos Roberto
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/87958
Acceso en línea:http://hdl.handle.net/11336/87958
Access Level:acceso abierto
Palabra clave:ARTIFICIAL PANCREAS
GLYCEMIC VARIABILITY
INSULIN ESTIMATION
KALMAN FILTER
STOCHASTIC MODEL
https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
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network_acronym_str AR
network_name_str Argentina
repository_id_str
dc.title.none.fl_str_mv Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques
title Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques
spellingShingle Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques
Avila, Luis Omar
ARTIFICIAL PANCREAS
GLYCEMIC VARIABILITY
INSULIN ESTIMATION
KALMAN FILTER
STOCHASTIC MODEL
https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
title_short Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques
title_full Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques
title_fullStr Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques
title_full_unstemmed Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques
title_sort Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques
dc.creator.none.fl_str_mv Avila, Luis Omar
de Paula, Mariano
Sánchez Reinoso, Carlos Roberto
author Avila, Luis Omar
author_facet Avila, Luis Omar
de Paula, Mariano
Sánchez Reinoso, Carlos Roberto
author_role author
author2 de Paula, Mariano
Sánchez Reinoso, Carlos Roberto
author2_role author
author
dc.subject.none.fl_str_mv ARTIFICIAL PANCREAS
GLYCEMIC VARIABILITY
INSULIN ESTIMATION
KALMAN FILTER
STOCHASTIC MODEL
https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
topic ARTIFICIAL PANCREAS
GLYCEMIC VARIABILITY
INSULIN ESTIMATION
KALMAN FILTER
STOCHASTIC MODEL
https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
description The ultimate goal of an artificial pancreas is finding the optimal insulin rates that can effectively reduce high blood glucose (BG) levels in type 1 diabetic patients. To achieve this, most closed-loop control strategies need to compute the optimal insulin action on the basis of precedent glucose and insulin levels. Unlike glucose levels which can be measured in real-time, unavailability of insulin sensors makes it essential the use of mathematical models to estimate plasma insulin concentrations. Between others, filtering techniques based on a generalization of the Kalman filter (KF) have been the most widely applied in the estimation of hidden states in nonlinear dynamic systems. Nevertheless, poor predictability of BG levels is a key issue since the glucose-insulin dynamics presents great inter- and intra-patient variability. Here, the question arises as to whether glycemic variability is not properly taken into account in models formulations and whether or it would compromise proper estimation of plasma insulin concentration. In order to tackle this point, a deterministic model describing glucose-insulin interaction plus a stochastic process to account for BG fluctuations were incorporated into the extended (EKF), cubature (CKF) and unscented (UKF) configurations of the Kalman filter to provide an estimate of the plasma insulin concentration. We found that for low glycemic variability, insulin state estimation can be attained with acceptable accuracy; however, as glycemic variability rises, Kalman filters rapidly degrade their performance as a consequence of large nonlinearities.
publishDate 2018
dc.date.none.fl_str_mv 2018-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/87958
Avila, Luis Omar; de Paula, Mariano; Sánchez Reinoso, Carlos Roberto; Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques; Elsevier; Biosystems; 171; 9-2018; 1-9
0303-2647
CONICET Digital
CONICET
url http://hdl.handle.net/11336/87958
identifier_str_mv Avila, Luis Omar; de Paula, Mariano; Sánchez Reinoso, Carlos Roberto; Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques; Elsevier; Biosystems; 171; 9-2018; 1-9
0303-2647
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0303264717302368
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.biosystems.2018.06.003
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1799194780039643136
spelling Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniquesAvila, Luis Omarde Paula, MarianoSánchez Reinoso, Carlos RobertoARTIFICIAL PANCREASGLYCEMIC VARIABILITYINSULIN ESTIMATIONKALMAN FILTERSTOCHASTIC MODELhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2The ultimate goal of an artificial pancreas is finding the optimal insulin rates that can effectively reduce high blood glucose (BG) levels in type 1 diabetic patients. To achieve this, most closed-loop control strategies need to compute the optimal insulin action on the basis of precedent glucose and insulin levels. Unlike glucose levels which can be measured in real-time, unavailability of insulin sensors makes it essential the use of mathematical models to estimate plasma insulin concentrations. Between others, filtering techniques based on a generalization of the Kalman filter (KF) have been the most widely applied in the estimation of hidden states in nonlinear dynamic systems. Nevertheless, poor predictability of BG levels is a key issue since the glucose-insulin dynamics presents great inter- and intra-patient variability. Here, the question arises as to whether glycemic variability is not properly taken into account in models formulations and whether or it would compromise proper estimation of plasma insulin concentration. In order to tackle this point, a deterministic model describing glucose-insulin interaction plus a stochastic process to account for BG fluctuations were incorporated into the extended (EKF), cubature (CKF) and unscented (UKF) configurations of the Kalman filter to provide an estimate of the plasma insulin concentration. We found that for low glycemic variability, insulin state estimation can be attained with acceptable accuracy; however, as glycemic variability rises, Kalman filters rapidly degrade their performance as a consequence of large nonlinearities.Fil: Avila, Luis Omar. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de Paula, Mariano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Sánchez Reinoso, Carlos Roberto. Universidad Nacional de Catamarca. Facultad Tecnología y Ciencias Aplicadas. Centro de Investigación y Desarrollo en Modelado, Simulación y Optimización de Sistemas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier2018-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/87958Avila, Luis Omar; de Paula, Mariano; Sánchez Reinoso, Carlos Roberto; Estimation of plasma insulin concentration under glycemic variability using nonlinear filtering techniques; Elsevier; Biosystems; 171; 9-2018; 1-90303-2647CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0303264717302368info:eu-repo/semantics/altIdentifier/doi/10.1016/j.biosystems.2018.06.003info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T13:36:15Zoai:ri.conicet.gov.ar:11336/87958instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982024-05-08 13:36:15.649CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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