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Titolo:
Multichannel electroencephalographic analyses via dynamic regression models with time-varying lag-lead structure
Autore:
Prado, R; West, M; Krystal, AD;
Indirizzi:
Univ Simon Bolivar, Dept Comp Cient & Estadist, Caracas, Venezuela Univ Simon Bolivar Caracas Venezuela ent & Estadist, Caracas, Venezuela Duke Univ, Durham, NC 27706 USA Duke Univ Durham NC USA 27706Duke Univ, Durham, NC 27706 USA
Titolo Testata:
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
, volume: 50, anno: 2001,
parte:, 1
pagine: 95 - 109
SICI:
0035-9254(2001)50:<95:MEAVDR>2.0.ZU;2-H
Fonte:
ISI
Lingua:
ENG
Soggetto:
ELECTROCONVULSIVE-THERAPY; SERIES; SEIZURES;
Keywords:
Bayesian inference; dynamic latent factors; dynamic linear models; electroconvulsive therapy; electroencephalography; markov chain monte carlo methods; non-stationary time series; time series decomposition;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
26
Recensione:
Indirizzi per estratti:
Indirizzo: Prado, R Univ Simon Bolivar, Dept Comp Cient & Estadist, Apartado 89000, Caracas, Venezuela Univ Simon Bolivar Apartado 89000 Caracas Venezuela , Venezuela
Citazione:
R. Prado et al., "Multichannel electroencephalographic analyses via dynamic regression models with time-varying lag-lead structure", J ROY STA C, 50, 2001, pp. 95-109

Abstract

Multiple time series of scalp electrical potential activity are generated routinely in electroencephalographic (EEG) studies. Such recordings provideimportant non-invasive data about brain function in human neuropsychiatricdisorders. Analyses of EEG traces aim to isolate characteristics of their spatiotemporal dynamics that may be useful in diagnosis, or may improve theunderstanding of the underlying neurophysiology or may improve treatment through identifying predictors and indicators of clinical outcomes. We discuss the development and application of nonstationary time series models for multiple EEG series generated from individual subjects in a clinical neuropsychiatric setting. The subjects are depressed patients experiencing generalized tonic-clonic seizures elicited by electroconvulsive therapy (ECT) as antidepressant treatment. Two varieties of models-dynamic latent factor models and dynamic regression models-are introduced and studied. We discuss model motivation acid form, and aspects of statistical analysis including parameter identifiability, posterior inference and implementation of these models via Markov chain Monte Carte techniques. In an application to the analysis of a typical set of 19 EEG series recorded during an ECT seizure at different locations over a patient's scalp, these models reveal time-varying features across the series that are strongly related to the placement of theelectrodes. We illustrate various model outputs, the exploration of such time-varying spatial structure and its relevance in the ECT study, and in basic EEG research in general.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 06/04/20 alle ore 08:19:57