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A novel direct feature-based seizure detector: Using the entropy of degree distribution of epileptic EEG signals

The electroencephalogram (EEG) signals with different brain states show different nonlinear dynamics. Recently the statistical properties of complex networks theory have been applied to explore the nonlinear dynamics of time series, which studies the dynamics of time series via its organization. This study combines the complex networks theory with epileptic EEG analysis and applies the statistical properties of complex networks to the automatic epileptic EEG detection. We construct the complex networks from the epileptic EEG series and then calculate the entropy of the degree distribution of the network (NDDE). The NDDE corresponding to the ictal EEG is lower than interictal EEG's. The experiment result shows that the approach using the NDDE as a classification feature obtains robust performance of epileptic seizure detection and the accuracy is up to 95.75%.