Rights statement: Copyright 2007 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.724697
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Research output: Contribution to Journal/Magazine › Journal article
| <mark>Journal publication date</mark> | 8/06/2007 |
|---|---|
| <mark>Journal</mark> | Proceedings of SPIE |
| Volume | 6602 |
| Number of pages | 12 |
| Publication Status | Published |
| <mark>Original language</mark> | English |
| Event | Conference on Noise and Fluctuations in Biological, Biophysical, and Biomedical Systems - Florence, Italy Duration: 21/05/2007 → 23/05/2007 |
| Conference | Conference on Noise and Fluctuations in Biological, Biophysical, and Biomedical Systems |
|---|---|
| Country/Territory | Italy |
| Period | 21/05/07 → 23/05/07 |
A Bayesian framework for parameter inference in non-stationary, nonlinear, stochastic, dynamical systems is introduced. It is applied to decode time variation of control parameters from time-series data modelling physiological signals. In this context a system of FitzHugh-Nagumo (FHN) oscillators is considered, for which synthetically generated signals are mixed via a measurement matrix. For each oscillator only one of the dynamical variables is assumed to be measured, while another variable remains hidden (unobservable). The control parameter for each FHN oscillator is varying in time. It is shown that the proposed approach allows one: (i) to reconstruct both unmeasured (hidden) variables of the FHN oscillators and the model parameters, (ii) to detect stepwise changes of control parameters for each oscillator, and (iii) to follow a continuous evolution of the control parameters in the quasi-adiabatic limit.