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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 I25123 BRESCIA ITALY POLITECN MILAN,DIPARTIMENTO BIOINGN I20133 MILAN ITALY UNIV MILAN,OSPED L SACCO I20157 MILAN ITALY
 Titolo Testata:
 IEEE transactions on biomedical engineering
fascicolo: 11,
volume: 44,
anno: 1997,
pagine: 1092  1101
 SICI:
 00189294(1997)44:11<1092:SDIMRB>2.0.ZU;2R
 Fonte:
 ISI
 Lingua:
 ENG
 Soggetto:
 ARTERIALPRESSURE; CLOSEDLOOP; 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. 10921101
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 closedloop interactions (clpoles) 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 clpoles 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 closedloop interactions. Application examples relevant to cardiovascular variability are briefly illustrated.
ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 10/07/20 alle ore 01:52:49