Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Sara Mohammadi; Ghasem Azemi
Volume 9, Issue 3 , December 2015, , Pages 215-227
Abstract
One of the most important newborn EEG abnormalities is the synchrony between different channels which, according to the clinical studies, can lead to neurological and neurodevelopmental outcomes in adulthood. This paper introduces a new method for automated detection of phase synchrony in multivariate ...
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One of the most important newborn EEG abnormalities is the synchrony between different channels which, according to the clinical studies, can lead to neurological and neurodevelopmental outcomes in adulthood. This paper introduces a new method for automated detection of phase synchrony in multivariate signals with applications to newborn EEG signals. In this method, first the instantaneous phase of each channel of the signal is estimated using Hilbert transform. In the case of EEG signals, due to their multicomponent nature, single-band signalsof the signal are needed to be extracted using a bank of band-pass filters. The synchronization between different channels of the signal is then quantitatively measured using a criterion based on the mutual information between instantaneous phases of theextracted single-band signals. The proposed method in this paper is then used to analyze, from synchronization point of view, multichannel EEG signals acquired from 5 newborns which include seizure-nonseizure periods and burst-suppression (B-S) patterns.Reciever operating curves (ROCs) are used to illustrate the performance of the method. The performance of the proposed method is also compared with that of the existing one based on the cointegration concept. Experimental results prove that the proposed method outperforms the existing one in measuring the generalized phase synchrony in multichannel newborn EEG signals. Also, results of analyzing seizure and nonseizure segments show that for all segmants there is a phase synchronization among EEG channels which is due to the connections between brain hemispheres in both cases. The results also show that seizure periods are more synchronous than nonseizure periods. The phase synchrony assessment of B-S patterns indicates that burst patterns are more synchronous than suppression patterns and there is a phase synchrony in both cases.
