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dc.contributor.author
Avila, Luis Omar
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dc.contributor.author
de Paula, Mariano
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dc.contributor.author
Martínez, Ernesto Carlos
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dc.contributor.author
Errecalde, Marcelo Luis
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dc.date.available
2019-12-05T19:15:58Z
dc.date.issued
2018-04
dc.identifier.citation
Avila, Luis Omar; de Paula, Mariano; Martínez, Ernesto Carlos; Errecalde, Marcelo Luis; Robust insulin estimation under glycemic variability using Bayesian filtering and Gaussian process models; Elsevier; Biomedical Signal Processing and Control; 42; 4-2018; 63-72
dc.identifier.issn
1746-8094
dc.identifier.uri
http://hdl.handle.net/11336/91524
dc.description.abstract
The ultimate goal of an artificial pancreas (AP) is finding the optimal insulin rates that can effectively reduce high blood glucose (BG) levels in type 1 diabetic patients. To achieve this, most autonomous closed-loop strategies continuously compute the optimal insulin bolus to be administrated on the basis of the estimated plasma concentrations for glucose and insulin. Unlike subcutaneous glucose levels which can be measured in real-time, unavailability of insulin sensors makes it essential the use of mathematical models so as to fully estimate plasma insulin concentrations. For model-based estimation, GP-Bayesian filters have been recently proposed to incorporate probabilistic non-parametric Gaussian process (GP) models of dynamic systems into Kalman filtering techniques. As a result, model uncertainty can explicitly be incorporated into the prediction step and in the filtering processes, which is usually not the case for more traditional filtering strategies that resort to parametric models for state estimation. More specifically, the question arises as to whether glycemic variability is properly taken into account in model formulations and whether it would compromise proper estimation of plasma insulin concentration. To tackle this, a stochastic glycemic model including variability was incorporated into different parametric and nonparametric filtering techniques to provide an estimate of the plasma insulin levels. In particular, we compared density representation against using knowledge about the parameterization of the transition dynamics and the observation function. We found that, as glycemic variability increases, filtering techniques based on parametric models rapidly degrades their performance as a consequence of large nonlinearities. Results show that Bayes’ filtering techniques increase predictability of the patient state, and thus, boost safety and performance in the AP control and monitoring tasks.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
BAYESIAN FILTERING
dc.subject
GAUSSIAN PROCESSES
dc.subject
GLYCEMIC VARIABILITY
dc.subject
PLASMA INSULIN ESTIMATION
dc.subject
STOCHASTIC MODEL
dc.subject.classification
Otras Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Robust insulin estimation under glycemic variability using Bayesian filtering and Gaussian process models
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-10-16T15:09:05Z
dc.journal.volume
42
dc.journal.pagination
63-72
dc.journal.pais
Países Bajos
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dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Avila, Luis Omar. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: de Paula, Mariano. Centro de Investigaciones En Física E Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
dc.description.fil
Fil: Errecalde, Marcelo Luis. Universidad Nacional de San Luis; Argentina
dc.journal.title
Biomedical Signal Processing and Control
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1746809418300260
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.bspc.2018.01.019
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