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

A. H. Omidvarnia, M. Mesbah, M. S. Khlif, J. M. O'Toole, P. B. Colditz, B. Boashash

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Multivariate Granger causality in the time-frequency 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.

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Electroencephalography
Kalman filters
Newborn Infant
Extended Kalman filters
Causality
Transfer functions
Brain
Seizures

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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title = "Kalman filter-based time-varying cortical connectivity analysis of newborn EEG.",
abstract = "Multivariate Granger causality in the time-frequency 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.",
author = "Omidvarnia, {A. H.} and M. Mesbah and Khlif, {M. S.} and O'Toole, {J. M.} and Colditz, {P. B.} and B. Boashash",
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AU - Omidvarnia, A. H.

AU - Mesbah, M.

AU - Khlif, M. S.

AU - O'Toole, J. M.

AU - Colditz, P. B.

AU - Boashash, B.

PY - 2011

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AB - Multivariate Granger causality in the time-frequency 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.

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