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dc.contributor.authorAl-Matouq, Ali Ahmed
dc.contributor.authorLaleg-Kirati, Taous-Meriem
dc.contributor.authorNovara, Carlo
dc.contributor.authorRabbone, Ivana
dc.contributor.authorVincent, Tyrone
dc.date.accessioned2019-07-04T11:15:44Z
dc.date.available2019-07-04T11:15:44Z
dc.date.issued2019-03-15
dc.identifier.citationAl-Matouq, A. A., Laleg-Kirati, T.-M., Novara, C., Rabbone, I., & Vincent, T. (2020). Sparse Reconstruction of Glucose Fluxes Using Continuous Glucose Monitors. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(5), 1797–1809. doi:10.1109/tcbb.2019.2905198
dc.identifier.doi10.1109/TCBB.2019.2905198
dc.identifier.urihttp://hdl.handle.net/10754/655914
dc.description.abstractA new technique for estimating postprandial glucose flux profiles without the use of glucose tracers is proposed. The technique assumes knowledge of patient parameters relevant to the glucose, insulin and endogoneous glucose production subsystems. A convex Lasso formulation is used to estimate the glucose fluxes that combines (1) the known patient parameters; (2) a sparse vector space encoding the space of plausible glucose flux profiles; (3) continuous glucose monitor measurements taken during the meal; (4) amount of insulin injected; (5) amount of meal carbohydrates and (6) an estimate of the initial conditions. Three glucose fluxes are estimated: glucose rate of appearance from the intestine; endogenous glucose production from the liver; insulin dependent glucose utilization and other important state variables. Sparse encoding of a large set of simulated glucose fluxes using the UVa Padova simulator is used so that a sparse representation of the space of plausible glucose flux profiles is obtained. The estimation technique was validated in both simulation and experiments on 3 T1DM patients undergoing the triple tracer meal protocol. The results indicate that the technique is capable of reproducing the triple tracer measurements while estimating the missing measurements for a certain model parameter selection.
dc.description.sponsorshipThis work was not supported by any organization
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8667648/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8667648
dc.rights(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectGlucose metabolism
dc.subjectContinuous glucose monitors
dc.subjectType 1 Diabetes
dc.subjectMeal tolerance test
dc.subjectSparse Encoding
dc.subjectLasso Estimation
dc.titleSparse Reconstruction of Glucose Fluxes using Continuous Glucose Monitors
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalIEEE/ACM Transactions on Computational Biology and Bioinformatics
dc.eprint.versionPost-print
dc.contributor.institutionEngineering Management, Prince Sultan University, 172459 Riyadh, Riyadh Saudi Arabia 11586
dc.contributor.institutionDepartment of Electronics and Telecommunications, Politecnico di Torino, 19032 Torino, Piemonte Italy
dc.contributor.institutionDepartment of Paediatrics, University of Turin, Turin, Turin Italy
dc.contributor.institutionElectrical Engineering and Computer Science, Colorado School of Mines, 3557 Golden, Colorado United States
kaust.personLaleg-Kirati, Taous-Meriem
refterms.dateFOA2019-07-04T11:16:37Z
dc.date.published-online2019-03-15
dc.date.published-print2020-09-01


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