Deconvolution of Infrequently Sampled Data for the Estimation of Growth Hormone Secretion

In this paper, 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. © 1995 IEEE