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Titolo:
SPECTRAL DECOMPOSITION IN MULTICHANNEL RECORDINGS BASED ON MULTIVARIATE PARAMETRIC IDENTIFICATION
Autore:
BASELLI G; PORTA A; RIMOLDI O; PAGANI M; CERUTTI S;
Indirizzi:
UNIV BRESCIA,DIPARTIMENTO ELETTRON & AUTOMAZ,VIA BRANZE 38 I-25123 BRESCIA ITALY POLITECN MILAN,DIPARTIMENTO BIOINGN I-20133 MILAN ITALY UNIV MILAN,OSPED L SACCO I-20157 MILAN ITALY
Titolo Testata:
IEEE transactions on biomedical engineering
fascicolo: 11, volume: 44, anno: 1997,
pagine: 1092 - 1101
SICI:
0018-9294(1997)44:11<1092:SDIMRB>2.0.ZU;2-R
Fonte:
ISI
Lingua:
ENG
Soggetto:
ARTERIAL-PRESSURE; CLOSED-LOOP; MODEL;
Keywords:
BIOMEDICAL SIGNAL PROCESSING; CARDIOVASCULAR VARIABILITY SIGNALS; MULTIVARIATE PARAMETRIC MODELS; MULTIVARIATE SPECTRAL DECOMPOSITION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
24
Recensione:
Indirizzi per estratti:
Citazione:
G. Baselli et al., "SPECTRAL DECOMPOSITION IN MULTICHANNEL RECORDINGS BASED ON MULTIVARIATE PARAMETRIC IDENTIFICATION", IEEE transactions on biomedical engineering, 44(11), 1997, pp. 1092-1101

Abstract

A method of spectral decomposition in multichannel recordings is proposed, which represents the results of multivariate (MV) parametric identification in terms of classification and quantification of differentoscillating mechanisms. For this purpose, a class of MV dynamic adjustment (MDA) models in which a MV autoregressive (MAR) network of causal interactions is fed by uncorrelated autoregressive (AR) processes isdefined. Poles relevant to the MAR network closed-loop interactions (cl-poles) and poles relevant to each AR input are disentangled and accordingly classified. The autospectrum of each channel can be divided into 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 meansof the residual method. Therefore, different oscillating mechanisms, even at similar frequencies, are classified by different poles and quantified by the corresponding components. The structure of MDA models is quite flexible and can be adapted to various sets of available signals and a priori hypotheses about the existing interactions; a graphical layout is proposed that emphasizes the oscillation sources and the corresponding closed-loop interactions. Application examples relevant to cardiovascular variability are briefly illustrated.

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Documento generato il 10/07/20 alle ore 01:52:49