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

doi:10.1109/IEMBS.2011.6090335

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

Annu Int Conf IEEE Eng Med Biol Soc. 2011:2011:1423-6. doi: 10.1109/IEMBS.2011.6090335.

Authors

A H Omidvarnia¹, M Mesbah, M S Khlif, J M O'Toole, P B Colditz, B Boashash

Affiliation

¹ Clinical Research Centre, The University of Queensland, Brisbane, Australia. a.omidvarnia@uq.edu.au

PMID: 22254585
DOI: 10.1109/IEMBS.2011.6090335

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.

MeSH terms

Algorithms*
Brain / physiology*
Brain Mapping / methods*
Computer Simulation
Data Interpretation, Statistical
Diagnosis, Computer-Assisted / methods*
Electroencephalography / methods*
Humans
Infant, Newborn
Models, Neurological*
Models, Statistical
Neonatal Screening / methods*
Reproducibility of Results
Sensitivity and Specificity