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.
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Acknowledgments
This research was supported by Strategic Research Grant of City University of Hong Kong (7002224) and Program for New Century Excellent Talents in University (NECT-07-0735).
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Ouyang, G., Dang, C., Li, X. (2011). Complexity Analysis of EEG Data with Multiscale Permutation Entropy. In: Wang, R., Gu, F. (eds) Advances in Cognitive Neurodynamics (II). Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9695-1_111
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DOI: https://doi.org/10.1007/978-90-481-9695-1_111
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