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Permutation Entropy,PE

algorithm

papers

  • G Ouyang, J Li, X Liu, X Li, Dynamic Characteristics of Absence EEG Recordings with Multiscale Permutation Entropy Analysis, Epilepsy Research, doi: 10.1016/j.eplepsyres.2012.11.003
  • G Ouyang, C Dang, X Li, Complexity Analysis of EEG Data with Multiscale Permutation Entropy, Advances in Cognitive Neurodynamics (II), 2011, pp 741-745
  • X Li, G Ouyang, D Richards, Predictability analysis of absence seizures with permutation entropy, Epilepsy Research, Vol. 77pp. 70-74, 2007
  • Unakafova, V.A., Keller, K., 2013. Efficiently measuring complexity on the basis of real-world Data. Entropy, 15(10), 4392-4415.
  • Bandt, C. and Pompe, B., 2002. Permutation entropy: a natural complexity measure for time series. Physical review letters, 88(17), p.174102.
  • Keller, K., Unakafov, A.M. and Unakafova, V.A., 2014. Ordinal patterns, entropy, and EEG. Entropy, 16(12), pp.6212-6239.
  • Riedl, M., Muller, A. and Wessel, N., 2013. Practical considerations of permutation entropy. The European Physical Journal Special Topics, 222(2), pp.249-262.
  • Zanin, M., Zunino, L., Rosso, O.A. and Papo, D., 2012. Permutation entropy and its main biomedical and econophysics applications: a review. Entropy, 14(8), pp.1553-1577.
  • Amigo, J.M., Zambrano, S. and Sanjuan, M.A., 2008. Combinatorial detection of determinism in noisy time series. EPL (Europhysics Letters), 83(6), p.60005.
  • Unakafova, V.A., 2015. Investigating measures of complexity for dynamical systems and for time series (Doctoral dissertation, University of Luebeck).
  • Keller, K., and M. Sinn. Ordinal analysis of time series. Physica A: Statistical Mechanics and its Applications 356.1 (2005): 114—120