title: Reconstructing insulin secretion rate after a glucose stimulus by an improved stochastic deconvolution method.
abstract: Reconstructing insulin secretion rate (ISR) after a glucose stimulus by deconvolution is difficult because of its biphasic pattern, i.e., a rapid secretion peak is followed by a slower release. Here, we refine a recently proposed stochastic deconvolution method by modeling ISR as the multiple integration of a white noise process with time-varying statistics. The unknown parameters are estimated from the data by employing a maximum likelihood criterion. A fast computational scheme implementing the method is presented. Monte Carlo simulation results are developed which numerically show a more reliable ISR profile reconstructed by the new method.
authors.affiliation: Dipartimento di Elettronica e Informatica, Università degli Studi di Padova, Italy.
authors.forename: G
authors.initials: G
authors.lastname: Pillonetto
authors.forename: G
authors.initials: G
authors.lastname: Sparacino
authors.forename: C
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D001698:Biomedical Engineering; D003920:Diabetes Mellitus / Q000175:diagnosis / Q000503:physiopathology; D005951:Glucose Tolerance Test / Q000706:statistics & numerical data; D006801:Humans; D007328:Insulin / Q000378:metabolism*; D000078790:Insulin Secretion; D008954:Models, Biological; D009010:Monte Carlo Method; D013269:Stochastic Processes
chemical_list: D007328:Insulin