Complexity Analysis of EEG Data with Multiscale Permutation Entropy

Abstract

In this study, we propose a powerful tool, called multiscale permutation entropy (MPE), to evaluate the dynamical characteristics of electroencephalogram (EEG) at the duration of epileptic seizure and seizure-free states. Numerical simulation analysis shows that MPE method is able to distinguish between the stochastic noise and deterministic chaotic data. The real EEG data analysis shows that a high entropy value is assigned to seizure-free EEG recordings and a low entropy value is assigned to seizure EEG recordings at the major scales. This result means that EEG signals are more complex in the seizure-free state than in the seizure state.