Kalman filter-based time-varying cortical connectivity analysis of newborn EEG

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Kalman filter-based time-varying cortical connectivity analysis of newborn EEG

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dc.contributor.author Omidvarnia, A.H.
dc.contributor.author Mesbah, M
dc.contributor.author Khlif, M.S.
dc.contributor.author O'Toole, J.M.
dc.contributor.author Colditz, P
dc.contributor.author Boashash, B
dc.date.accessioned 2012-06-18T08:28:35Z
dc.date.available 2012-06-18T08:28:35Z
dc.date.issued 2011
dc.identifier.citation A. H. Omidvarnia, M. Mesbah, M. S. Khlif, J. M. O'Toole, P. B. Colditz, and B. Boashash, "Kalman filter-based time-varying cortical connectivity analysis of newborn EEG," in Proc. of IEEE 2011 Annual International Conference of Engineering in Medicine and Biology Society,EMBC, 2011, pp. 1423-1426 en_US
dc.identifier.uri http://dx.doi.org/10.1109/iembs.2011.6090335
dc.identifier.uri http://hdl.handle.net/10576/10847
dc.description This paper presents a time-varying cortical connectivity analysis for to a source localization approach within the inner layers of the newborn brain. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).
dc.description.abstract Multivariate Granger causality in the timefrequency domain as a representation of time-varying cortical connectivity in the brain has been investigated for the adult case. This is, however, not the case in newborns as the nature of the transient changes in the newborn EEG is different from that of adults. This paper aims to evaluate the performance of the time-varying versions of the two popular Granger causality measures, namely Partial Directed Coherence (PDC) and direct Directed Transfer Function (dDTF). The parameters of the time-varying AR, that models the inter-channel interactions, are estimated using Dual Extended Kalman Filter (DEKF) as it accounts for both non-stationarity and non-linearity behaviors of the EEG. Using simulated data, we show that fast changing cortical connectivity between channels can be measured more accurately using the time-varying PDC. The performance of the time-varying PDC is also tested on a neonatal EEG exhibiting seizure. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject DEKF
dc.subject EEG nonlinearity behavior
dc.subject EEG nonstationarity behavior
dc.subject Granger causality measures
dc.subject dDTF
dc.subject directed transfer function
dc.subject dual extended Kalman filter
dc.subject fast changing cortical connectivity
dc.subject interchannel interactions
dc.subject neonatal EEG exhibiting seizure
dc.subject newborn EEG
dc.subject partial directed coherence
dc.subject time varying AR parameters
dc.subject time varying PDC
dc.subject time varying cortical connectivity analysis
dc.subject time-frequency domain multivariate Granger causality
dc.title Kalman filter-based time-varying cortical connectivity analysis of newborn EEG en_US
dc.type Article en_US

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