Title :
Deconvolution of infrequently sampled data for the estimation of growth hormone secretion
Author :
De Nicolao, Giuseppe ; Liberati, Diego ; Sartorio, Alessandro
Author_Institution :
Dipartimento di Inf. e Sistemistica, Pavia Univ., Italy
fDate :
7/1/1995 12:00:00 AM
Abstract :
The deconvolution of infrequently and nonuniformly sampled data is addressed. A nonparametric technique is worked out that provides a smooth estimate of the unknown input signal and takes into account nonnegativity constraints. In spite of the size of the problem, efficient algorithms for solving the constrained optimization problem and computing confidence intervals are proposed. The new technique is used to estimate growth hormone (GH) secretion after repeated GH-releasing hormone (GHRH) administration from samples of blood concentration.
Keywords :
blood; data analysis; deconvolution; optimisation; signal sampling; blood concentration; confidence intervals computation; constrained optimization problem; efficient algorithms; growth hormone secretion estimation; infrequently nonuniformly sampled data; infrequently sampled data deconvolution; nonnegativity constraints; Biochemistry; Blood; Constraint optimization; Convolution; Deconvolution; Endocrine system; Fluids and secretions; Integral equations; Sampling methods; Signal processing algorithms; Adult; Algorithms; Computer Simulation; Confidence Intervals; Galanin; Growth Hormone; Growth Hormone-Releasing Hormone; Humans; Models, Biological; Neuropeptides; Peptides; Statistics, Nonparametric;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
