Ordinal pattern based similarity analysis for EEG recordings

Abstract

Ordinal patterns analysis such as permutation entropy of the EEG series has been found to usefully track brain dynamics and has been applied to detect changes in the dynamics of EEG data. In order to further investigate hidden nonlinear dynamical characteristics in EEG data for differentiating brain states, this paper proposes a novel dissimilarity measure based on the ordinal pattern distributions of EEG series. Given a segment of EEG series, we first map this series into a phase space, then calculate the ordinal sequences and the distribution of these ordinal patterns. Finally, the dissimilarity between two EEG series can be qualified via a simple distance measure. A neural mass model was proposed to simulate EEG data and test the performance of the dissimilarity measure based on the ordinal patterns distribution. Furthermore, this measure was then applied to analyze EEG data from 24 Genetic Absence Epilepsy Rats from Strasbourg (GAERS), with the aim of distinguishing between interictal, preictal and ictal states. The dissimilarity measure of a pair of EEG signals within the same group and across different groups was calculated, respectively. As expected, the dissimilarity measures during different brain states were higher than internal dissimilarity measures. When applied to the preictal detection of absence seizures, the proposed dissimilarity measure successfully detected the preictal state prior to their onset in 109 out of 168 seizures (64.9%). Our results showed that dissimilarity measures between EEG segments during the same brain state were significant smaller that those during different states. This suggested that the dissimilarity measure, based on the ordinal patterns in the time series, could be used to detect changes in the dynamics of EEG data. Moreover, our results suggested that ordinal patterns in the EEG might be a potential characteristic of brain dynamics. This dissimilarity measure is a promising method to reveal dynamic changes in EEG, for example as occur in the transition of epileptic seizures. This method is simple and fast, so might be applied in designing an automated closed-loop seizure prevention system for absence epilepsy.