Multifractional Property Analysis of Human Sleep EEG signals

Electroencephalogram (EEG), the measures and records of the electrical activity of the brain, exhibits evidently nonlinear, nonstationary, chaotic and complex dynamic properties. Based on these properties, many nonlinear dynamical analysis techniques have emerged, and much valuable information has been extracted from complex EEG signals using these nonlinear analysis techniques. Among these techniques, the Hurst exponent estimation was widely used to characterize the fractional or scaling property of the EEG signals. However, the constant Hurst exponent H cannot capture the detailed information of dynamic EEG signals. In this research, the multifractional property of the normal human sleep EEG signals is investigated and characterized using local Holder exponent H(t). The comparison of the analysis results for human sleep EEG signals in different stages using constant Hurst exponent H and the local Holder exponent H(t) are summarized with tables and figures in the paper. The results of the analysis show that local Holder exponent provides a novel and valid tool for dynamic assessment of brain activities in different sleep stages.

[1]  T. Ferrée,et al.  Fluctuation Analysis of Human Electroencephalogram , 2001, physics/0105029.

[2]  Azer Bestavros,et al.  Self-similarity in World Wide Web traffic: evidence and possible causes , 1996, SIGMETRICS '96.

[3]  Jan Beran,et al.  Statistics for long-memory processes , 1994 .

[4]  Yangquan Chen,et al.  Tracking performance and robustness analysis of Hurst estimators for multifractional processes , 2012, IET Signal Process..

[5]  Ming Li,et al.  Correlation matching method for the weak stationarity test of LRD traffic , 2010, Telecommun. Syst..

[6]  Houssain Kettani,et al.  A novel approach to the estimation of the long-range dependence parameter , 2006, IEEE Transactions on Circuits and Systems II: Express Briefs.

[7]  K. Linkenkaer-Hansen,et al.  Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations , 2001, The Journal of Neuroscience.

[8]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[9]  Y. Chen,et al.  Fractional Processes and Fractional-Order Signal Processing: Techniques and Applications , 2011 .

[10]  C. L. Martínez-González,et al.  Fractal Analysis of EEG Signals in the Brain of Epileptic Rats, with and without Biocompatible Implanted Neuroreservoirs , 2009 .

[11]  R. Acharya U,et al.  Nonlinear analysis of EEG signals at different mental states , 2004, Biomedical engineering online.

[12]  Md Nurujjaman,et al.  Comparative study of nonlinear properties of EEG signals of normal persons and epileptic patients , 2007, Nonlinear biomedical physics.

[13]  Ivan Osorio,et al.  Hurst parameter estimation for epleptic seizure detection , 2007, Commun. Inf. Syst..

[14]  Tianshuang Qiu,et al.  On the robustness of Hurst estimators , 2011 .

[15]  Bruce J. West,et al.  The dynamics of EEG entropy , 2009, Journal of biological physics.

[16]  R. Peltier,et al.  Multifractional Brownian Motion : Definition and Preliminary Results , 1995 .

[17]  K. Susmáková,et al.  Human Sleep and Sleep EEG , 2005 .

[18]  Renaud Marty,et al.  Invariance principle, multifractional gaussian processes and long-range dependence , 2008 .

[19]  E. Wolpert.  A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[20]  G. Moody,et al.  Development of the polysomnographic database on CD‐ROM , 1999, Psychiatry and clinical neurosciences.

[21]  A. Loomis,et al.  Cerebral states during sleep, as studied by human brain potentials , 1937 .

[22]  Benjamin M. Tabak,et al.  Time-varying long-range dependence in US interest rates , 2007 .

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

[24]  Ming Li,et al.  Representation of a Stochastic Traffic Bound , 2010, IEEE Transactions on Parallel and Distributed Systems.

[25]  U. Rajendra Acharya,et al.  Non-linear analysis of EEG signals at various sleep stages , 2005, Comput. Methods Programs Biomed..

[26]  K. Falconer.  The Local Structure of Random Processes , 2003 .

[27]  F. Serinaldi.  Use and misuse of some Hurst parameter estimators applied to stationary and non-stationary financial time series , 2010 .

[28]  A. Chesson,et al.  The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications , 2007 .