Complexity Analysis of Physiological Time Series Using a Novel Permutation-Ratio Entropy

To date, various information entropy methods have been employed to evaluate complexity within physiological time series. However, such methods cannot discern different levels of nonlinear chaotic properties within time series, indicating that incorrect results are yielded due to noise. Herein, a novel permutation-ratio entropy (PRE) method was proposed and compared with the classical permutation entropy (PE) method, multiscale PE with scale factors 4 and 8 (MPE_S4 and MPE_S8). Simulations with clean logistic mapping series and the logistic mapping series plus noise with a signal-to-noise ratio of 20 dB showed that only PRE monotonically declined with complexity reduction within time series for all 12 combinations of parameters (time delay <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> and embedded dimension <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>). By contrast, PE only monotonically decreased at three parameter combinations for the clean logistic series and failed at all 12 parameter combinations for the logistic series plus noise, and moreover, MPE_S4 and MPE_S8 failed to monotonically decline for the clean logistic series and the logistic series plus noise at all parameter combinations. Results of surrogate data analysis indicated that PRE could more effectively measure the deterministic components of nonlinear within time series than PE, MPE_S4 and MPE_S8. In addition, the parameter <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> could enable PE, MPE_S4, and MPE_S8 to yield incorrect results, but it could not do so for PRE. Both PRE and PE were relatively stable on various parameters of <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula>. Interictal and ictal electroencephalography (EEG) recordings from the Bonn database and the CHB-MIT scalp EEG database were also observed, and the results indicated that the PRE could accurately measure the complexity of EEG recordings, as shown by higher entropy values yielded from interictal intracranial EEG recordings versus those yielded from ictal ones (<inline-formula> <tex-math notation="LaTeX">$p < 0.01$ </tex-math></inline-formula>).

[1]  Yi Yin,et al.  Weighted permutation entropy based on different symbolic approaches for financial time series , 2016 .

[2]  Richard Heusdens,et al.  Analysis and Synthesis of Pseudo-Periodic Job Arrivals in Grids: A Matching Pursuit Approach , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

[3]  Niels Wessel,et al.  Practical considerations of permutation entropy , 2013, The European Physical Journal Special Topics.

[4]  Ali Karimpour,et al.  Fast and Robust Detection of Epilepsy in Noisy EEG Signals Using Permutation Entropy , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

[5]  L M Hively,et al.  Detecting dynamical changes in time series using the permutation entropy. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Jun Wang,et al.  Multiscale permutation entropy analysis of electrocardiogram , 2017 .

[7]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[8]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  G. Consolini,et al.  Permutation entropy analysis of complex magnetospheric dynamics , 2014 .

[10]  Richard Taylor Interpretation of the Correlation Coefficient: A Basic Review , 1990 .

[11]  Junfeng Wang,et al.  Improved method for detecting weak abrupt information based on permutation entropy , 2017 .

[12]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[13]  Zhenhu Liang,et al.  Multiscale permutation entropy analysis of EEG recordings during sevoflurane anesthesia , 2010, Journal of neural engineering.

[14]  Ali H. Shoeb,et al.  Application of machine learning to epileptic seizure onset detection and treatment , 2009 .

[15]  Qianli D. Y. Ma,et al.  Modified permutation-entropy analysis of heartbeat dynamics. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Hong-Bo Xie,et al.  Complexity analysis of the biomedical signal using fuzzy entropy measurement , 2011, Appl. Soft Comput..

[17]  U. Rajendra Acharya,et al.  Entropies for detection of epilepsy in EEG , 2005, Comput. Methods Programs Biomed..

[18]  M. Mukaka,et al.  Statistics corner: A guide to appropriate use of correlation coefficient in medical research. , 2012, Malawi medical journal : the journal of Medical Association of Malawi.

[19]  Ruqiang Yan,et al.  Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines , 2012 .

[20]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[21]  Julius Georgiou,et al.  Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines , 2012, Expert Syst. Appl..

[22]  Hamed Azami,et al.  Improved multiscale permutation entropy for biomedical signal analysis: Interpretation and application to electroencephalogram recordings , 2015, Biomed. Signal Process. Control..

[23]  Badong Chen,et al.  Weighted-permutation entropy: a complexity measure for time series incorporating amplitude information. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.