
Mathematical Biosciences and Engineering, 2019, 16(6): 68426857. doi: 10.3934/mbe.2019342.
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Permutation entropy: Influence of amplitude information on time series classification performance
Technological Institute of Informatics(ITI), Universitat Politècnica de València, Campus Alcoi, Plaza Ferrándiz y Carbonell, 2, 03801, Alcoi, Spain
Received: , Accepted: , Published:
Special Issues: Algorithm Optimization for Big Data Applications in Computational Biology
Keywords: Permutation entropy; amplitude aware permutation entropy; fine–grained permutation entropy; weighted permutation entropy; sample entropy; time series classification
Citation: David Cuesta–Frau. Permutation entropy: Influence of amplitude information on time series classification performance. Mathematical Biosciences and Engineering, 2019, 16(6): 68426857. doi: 10.3934/mbe.2019342
References:
 1. R. Alcaraz, D. Abásolo, R. Hornero, et al., Study of Sample Entropy ideal computational parameters in the estimation of atrial fibrillation organization from the ECG, in 2010 Computing in Cardiology, 2010, 1027–1030.
 2. R. G. Andrzejak, K. Lehnertz, F. Mormann, et al., Indications of nonlinear deterministic and finitedimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Phys. Rev. E, 64 (2001), 061907.
 3. K. N. Aronis, R. D. Berger, H. Calkins, et al., Is human atrial fibrillation stochastic or deterministic? Insights from missing ordinal patterns and causal entropy–complexity plane analysis, Chaos, 28 (2018), 063130.
 4. H. Azami and J. Escudero, Amplitudeaware permutation entropy: Illustration in spike detection and signal segmentation, Comput. Meth. Prog. Bio., 128 (2016), 40–51.
 5. A. Bagnall, J. Lines, A. Bostrom, et al., The great time series classification bake off: A review and experimental evaluation of recent algorithmic advances, Data Min. Knowl. Disc., 31 (2017), 606–660.
 6. C. Bandt and B. Pompe, Permutation entropy: A natural complexity measure for time series, Phys. Rev. Lett., 88 (2002), 174102.
 7. C. Bian, C. Qin, Q. D. Y. Ma, et al., Modified Permutationentropy analysis of heartbeat dynamics, Phys. Rev. E, 85 (2012), 021906.
 8. A. E. X. Brown, E. I. Yemini, L. J. Grundy, et al., A dictionary of behavioral motifs reveals clusters of genes affecting caenorhabditis elegans locomotion, Proceed. Nat. Aca. Sci., 110 (2013), 791–796.
 9. C. CarricarteNaranjo, D. J. Cornforth, L. M. SánchezRodríguez, et al., Rényi and permutation entropy analysis for assessment of cardiac autonomic neuropathy, in EMBEC & NBC 2017 (eds. H. Eskola, O. Väisänen, J. Viik and J. Hyttinen), Springer Singapore, Singapore, 2018, 755–758.
 10. E. CirugedaRoldán, D. CuestaFrau, P. MiróMartínez, et al., A new algorithm for quadratic sample entropy optimization for very short biomedical signals: Application to blood pressure records, Comput. Meth. Prog. Bio., 114 (2014), 231–239.
 11. E. CirugedaRoldán, D. Novák, V. Kremen, et al., Characterization of complex fractionated atrial electrograms by Sample Entropy: An international multi–center study, Entropy, 17 (2015), 7493–7509.
 12. D. CuestaFrau, P. MiróMartínez, S. OltraCrespo, et al., Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm, Comput. Meth. Prog. Bio., 165 (2018), 197–204.
 13. D. CuestaFrau, P. MiróMartínez, S. OltraCrespo, et al., Model selection for body temperature signal classification using both amplitude and ordinalitybased entropy measures, Entropy, 20.
 14. D. CuestaFrau, D. Novák, V. Burda, et al., Characterization of artifact influence on the classification of glucose time series using sample entropy statistics, Entropy, 20.
 15. D. CuestaFrau, M. VarelaEntrecanales, A. MolinaPicó, et al., Patterns with equal values in permutation entropy: Do they really matter for biosignal classification?, Complexity, 2018 (2018), 1–15.
 16. B. Deng, L. Cai, S. Li, et al., Multivariate multiscale weighted Permutation Entropy analysis of EEG complexity for Alzheimer's disease, Cogn. Neurodynamics, 11 (2017), 217–231.
 17. B. Fadlallah, B. Chen, A. Keil, et al., Weightedpermutation entropy: A complexity measure for time series incorporating amplitude information, Phys. Rev. E, 87 (2013), 022911.
 18. Y. Gao, F. Villecco, M. Li, et al., Multiscale permutation entropy based on improved LMD and HMM for rolling bearing diagnosis, Entropy, 19.
 19. J. Garland, T. R. Jones, M. Neuder, et al., Anomaly detection in paleoclimate records using permutation entropy, Entropy, 20.
 20. A. L. Goldberger, L. A. N. Amaral, L. Glass, et al., PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals, Circulation, 101 (2000), 215–220.
 21. M. Henry and G. Judge, Permutation entropy and information recovery in nonlinear dynamic economic time series, Econometrics, 7.
 22. N. Iyengar, C. K. Peng, R. Morin, et al., Agerelated alterations in the fractal scaling of cardiac interbeat interval dynamics, Am. J. Physiol. Integrat. Comparat. Physiol., 271 (1996), R1078–R1084, PMID: 8898003.
 23. D. E. Lake, J. S. Richman, M. P. Griffin, et al., Sample entropy analysis of neonatal heart rate variability, Am. J. Physiol. Integrat. Comparat. Physiol., 283 (2002), R789–R797.
 24. H. Li, C. Peng and D. Ye, A study of sleep staging based on a sample entropy analysis of electroencephalogram, BioMed. Mater. Eng., 26 (2015), S1149–S1156.
 25. G. B. Moody, A. L. Goldberger, S. McClennenm, et al., Predicting the onset of Paroxysmal Atrial Fibrillation: The Computers in Cardiology Challenge 2001, Comput. Cardiol., 28 (2011), 113–116.
 26. D. Murray, J. Liao, L. Stankovic, et al., A data management platform for personalised realtime energy feedback, in Procededings of the 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting, 2015.
 27. N. Nicolaou and J. Georgiou, The use of permutation entropy to characterize sleep electroencephalograms, Clin. EEG Neurosci., 42 (2011), 24–28, PMID: 21309439.
 28. E. Olofsen, J. Sleigh and A. Dahan, Permutation entropy of the electroencephalogram: A measure of anaesthetic drug effect, Br. J. Anaesth., 101 (2008), 810–821.
 29. A. G. RaveloGarcía, J. L. NavarroMesa, CasanovaBlancas, et al., Application of the permutation entropy over the heart rate variability for the improvement of electrocardiogrambased sleep breathing pause detection, Entropy, 17 (2015), 914–927.
 30. J. S. Richman, Sample entropy statistics and testing for order in complex physiological signals, Commun. Stat. Theor. M., 36 (2007), 1005–1019.
 31. M. O. Sokunbi, Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets, Front. Neuroinform., 8 (2014), 69.
 32. X. Wang, S. Si, Y. Wei, et al., The optimized multiscale permutation entropy and its application in compound fault diagnosis of rotating machinery, Entropy, 21.
 33. Y. Xia, L. Yang, L. Zunino, et al., Application of permutation entropy and permutation minentropy in multiple emotional states analysis of RRI time series, Entropy, 20 (2018), 148.
 34. X. F. Liu and Y. Wang, Finegrained permutation entropy as a measure of natural complexity for time series, Chinese Phys. B, 18 (2009), 2690.
 35. Y. Yang, M. Zhou, Y. Niu, et al., Epileptic seizure prediction based on permutation entropy, in Front. Comput. Neurosci., 2018.
 36. E. Yemini, T. Jucikas, L. J. Grundy, et al., A database of caenorhabditis elegans behavioral phenotypes, Nat. Meth., 10 (2013), 877–879.
 37. M. Zanin, D. GómezAndrés, I. PulidoValdeolivas, et al., Characterizing normal and pathological gait through permutation entropy, Entropy, 20.
 38. M. Zanin, L. Zunino, O. A. Rosso, et al., Permutation entropy and its main biomedical and econophysics applications: A review, Entropy, 14 (2012), 1553–1577.
 39. Y. Zhang and P. Shang, Permutation entropy analysis of financial time series based on hills diversity number, Commun. Nonlinear Sci. Numer. Simul., 53 (2017), 288–298.
 40. L. Zunino and C. W. Kulp, Detecting nonlinearity in short and noisy time series using the permutation entropy, Phys. Lett. A, 381 (2017), 3627–3635.
 41. L. Zunino, F. Olivares, F. Scholkmann, et al., Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions, Phys. Lett. A, 381 (2017), 1883–1892.
 42. L. Zunino, M. Zanin, B. M. Tabak, et al., Forbidden patterns, permutation entropy and stock market inefficiency, Physica A, 388 (2009), 2854–2864.
This article has been cited by:
 1. David CuestaFrau, Antonio MolinaPicó, Borja Vargas, Paula González, Permutation Entropy: Enhancing Discriminating Power by Using Relative Frequencies Vector of Ordinal Patterns Instead of Their Shannon Entropy, Entropy, 2019, 21, 10, 1013, 10.3390/e21101013
 2. David CuestaFrau, Slope Entropy: A New Time Series Complexity Estimator Based on Both Symbolic Patterns and Amplitude Information, Entropy, 2019, 21, 12, 1167, 10.3390/e21121167
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