A. Belghith and C. Collet (France)
Deconvolution, respiratory signal, Generalized Gaussiandistribution, Copulas theory, Biomedical.
In this paper we address the problem of restoration of the wavelet coefficients related to crackle assumed to be a pulse respiratory signal with a non-gaussian colored noise. This task, requiring multivariate probability density computa tions for the data likelihood term, often faces with the lack of analytical multidimensional expressions in the non gaussian case. Thus, multidimensional Gaussian distribu tion is usually used for its simplicity, even if Gaussian as sumption is not always verified. In this work, we propose a new approach based on copula theory to compute mul tivariate generalized Gaussian marginals to deal with the non-gaussianity of the wavelet coefficients of the colored added noise for restoration of the respiratory signals.
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