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

Giuseppe De Nicolao, Diego Liberati, Alessandro Sartorio

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)678-687
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume42
Issue number7
DOIs
Publication statusPublished - 1995

ASJC Scopus subject areas

  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Deconvolution of Infrequently Sampled Data for the Estimation of Growth Hormone Secretion'. Together they form a unique fingerprint.

Cite this