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Nonparametric deconvolution of hormone time-series: A state-space approach


G. De Nicolao, G. Ferrari-Trecate, M. Franzosi

IEEE Conference on Control Application, Trieste, Italy, September 1-4.

The instantaneous secretion rate (ISR) of endocrine glands is not directly measurable and it can be recon-structed only indirectly by applying deconvolution algo-rithms to time-series of plasma hormone concentrations. In particular, nonparametric regularization-based decon-volution hinges on a variational problem whose solution is usually approximated by discretizing the continuous-time axis. The paper shows how to perform regularized decon-volution avoiding any form of discretization. In view of the equivalence between regularization and Bayesian es-timation, it is seen that the estimated ISR is a weighted sum of N basis functions, where N is the number of data. State-space methods are used to derive analytically the ba-sis functions as well as the entries of the matrix of the lin-ear system used to compute the weights. Alternatively, the weights can be computed in O(N) operations by a suitable algorithm based on Kalman filtering. As an illustration of the method, we estimate the spontaneous pulsatile ISR of luteinizing hormone (LH) from time series of plasma LH concentrations sampled every 5 min.


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% Autogenerated BibTeX entry
@InProceedings { NicFer:1998:IFA_1795,
    author={G. De Nicolao and G. Ferrari-Trecate and M. Franzosi},
    title={{Nonparametric deconvolution of hormone time-series: A
	  state-space approach}},
    booktitle={IEEE Conference on Control Application},
    address={Trieste, Italy},
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