title: Linear and nonlinear techniques for the deconvolution of hormone time-series.
abstract: Pulsatile hormone secretion is usually investigated by measuring hormone concentration in samples of peripheral plasma. In this paper, the deconvolution of hormone time-series to reconstruct the instantaneous secretion rate of glands is considered. Various techniques are discussed and compared in order to overcome the ill-conditioning of the problem and reduce the computational burden. In particular, linear techniques based on least squares, maximum a posteriori (MAP) estimation, and Wiener filtering are compared. A new nonlinear MAP estimator that keeps into account the non-Gaussian distribution of the unknown signal is worked out and shown to yield the best results. The performances of the algorithms are tested on simulated time-series as well as on series of Luteinizing Hormone (LH).
authors.affiliation: Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy.
authors.forename: G
authors.initials: G
authors.lastname: De Nicolao
authors.forename: D
authors.initials: D
authors.lastname: Liberati
mesh_terms: D000465:Algorithms; D015982:Bias; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D007986:Luteinizing Hormone / Q000097:blood / Q000378:metabolism* / Q000493:pharmacokinetics*; D008297:Male; D008657:Metabolic Clearance Rate; D015233:Models, Statistical; D016011:Normal Distribution; D016012:Poisson Distribution; D011673:Pulsatile Flow; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors
chemical_list: D007986:Luteinizing Hormone