SPECTRAL DECOMPOSITION IN MULTICHANNEL RECORDINGS BASED ON MULTIVARIATE PARAMETRIC IDENTIFICATION

A method of spectral decomposition in multichannel recordings is propo sed, which represents the results of multivariate (MV) parametric iden tification in terms of classification and quantification of different oscillating mechanisms. For this purpose, a class of MV dynamic adjust ment (MDA) models in which a MV autoregressive (MAR) network of causal interactions is fed by uncorrelated autoregressive (AR) processes is defined. Poles relevant to the MAR network closed-loop interactions (c l-poles) and poles relevant to each AR input are disentangled and acco rdingly classified. The autospectrum of each channel can be divided in to partial spectra each relevant to an input. Each partial spectrum is affected by the cl-poles and by the poles of the corresponding input; consequently, it is decomposed into the relevant components by means of the residual method. Therefore, different oscillating mechanisms, e ven at similar frequencies, are classified by different poles and quan tified by the corresponding components. The structure of MDA models is quite flexible and can be adapted to various sets of available signal s and a priori hypotheses about the existing interactions; a graphical layout is proposed that emphasizes the oscillation sources and the co rresponding closed-loop interactions. Application examples relevant to cardiovascular variability are briefly illustrated.