Multiplexing Difference in Epilepsy under sleep and wakefulness condition

Epilepsy is a fatal brain disease which affects nervous system and even partial epilepsy involves in global changes around the brain. Electroencephalogram (EEG) signals records the post-synaptic potentials of hundreds of neurons in the brain, which contains multiple source regions and offers subjective representation of brain condition. Complex network and graph theory are adopted for quantitative measurement exploitation. In this paper, patients with double temporal lobe epilepsy (dTLE) are enrolled and their EEG signals under sleep as well as awake condition are recorded. In order to detect functional connectivity, phase locking value (PLV) is brought in for measuring narrow-band interactions. Then the consensus clustering algorithm is implemented to separate individual nodes and dominant sets for discovering representative modular structure. Experimental results demonstrate that the cohesion in community member becomes tighter in dTLE group than controls. What's more, delta band, alpha band and lower gamma band renders alterations according to the variation of information analysis on subgraph distance. These results indicate that modular structure discovery and quantifications offers another choice of biomarkers for epilepsy in clinical diagnosis.