A Graph Theory Analysis on Distinguishing EEG-Based Brain Death and Coma

Electroencephalogram (EEG) is always used to diagnosis the patients consciousness clinically because it is safe and easy to be record from patients. The aim of this paper is to analysis the relations between each channel in order to find out the brain network of brain death and coma patients particularity. In this paper, we use 10 adult patients’ EEG data to calculate the partial directed coherence (PDC) and build the average brain network for the two groups’ data after t-test based on the PDC results. Results showed that, these two clinical data are at most difference in the network parameters of degree, centrality and cluster coefficient as the threshold of PDC is set of 0.3. The time-varying connectivity could lead to better understanding of non-symmetric relations between different EEG channels and application in prediction of patients in brain death or coma state.

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