Auto Mutual Information Analysis with Order Patterns for Epileptic EEG.

In this study, we investigated auto mutual information (AMI), based on order patterns analysis, as a tool to evaluate the dynamical characteristics of electroencephalogram (EEG) during interictal, preictal and ictal phase, respectively. Permutation entropy quantifies regularity in time series, while AMI detects the mutual information (MI) between a time series and a delayed version of itself. The results show that AMI method was able to reveal that the highest entropy values were assigned to interictal EEG recordings and the lowest entropy values were assigned to ictal EEG recordings. The classification ability of the AMI measures is tested using ANFIS classifier. Test results confirm that AMI method has potential in classifying the epileptic EEG signals.