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

Autores
Avila, Luis Omar; de Paula, Mariano; Sánchez Reinoso, Carlos Roberto
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
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; Argentina
Fil: 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; Argentina
Fil: 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; Argentina
Materia
ARTIFICIAL PANCREAS
GLYCEMIC VARIABILITY
INSULIN ESTIMATION
KALMAN FILTER
STOCHASTIC MODEL
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/87958
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/87958
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
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 Técnicas2025-09-03T09:46:20Zoai: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:34982025-09-03 09:46:20.319CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
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
topic ARTIFICIAL PANCREAS
GLYCEMIC VARIABILITY
INSULIN ESTIMATION
KALMAN FILTER
STOCHASTIC MODEL
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv 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.
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; Argentina
Fil: 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; Argentina
Fil: 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; Argentina
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
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
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
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score 13.13397

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