TY - JOUR
T1 - Deconvolution of Infrequently Sampled Data for the Estimation of Growth Hormone Secretion
AU - De Nicolao, Giuseppe
AU - Liberati, Diego
AU - Sartorio, Alessandro
PY - 1995
Y1 - 1995
N2 - 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.
AB - 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.
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U2 - 10.1109/10.391166
DO - 10.1109/10.391166
M3 - Article
C2 - 7542624
AN - SCOPUS:0029329127
SN - 0018-9294
VL - 42
SP - 678
EP - 687
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 7
ER -