A comparison analysis of embedding dimensions between normal and epileptic EEG time series.

The embedding dimensions of normal and epileptic electroencephalogram (EEG) time series are analyzed by two different methods, Cao's method and differential entropy method. The results of the two methods indicate consistently that the embedding dimensions of EEG signals during seizure will change and become different from that of normal EEG signals, and the embedding dimensions will vary intensively during seizure, whereas the embedding dimensions of normal EEG signals basically maintains stability. The embedding dimension results also reflect the variation of freedom degree of the human brain nonlinear dynamic system (NDS) during seizure. And based on the results of Cao's method, it is also found that normal EEG signals are of some degree of randomness, whereas epileptic EEG signals have determinism.

[1]  Christopher Essex,et al.  Chaotic time series analyses of epileptic seizures , 1990 .

[2]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[3]  J. Martinerie,et al.  Epileptic seizures can be anticipated by non-linear analysis , 1998, Nature Medicine.

[4]  Danilo P. Mandic,et al.  A differential entropy based method for determining the optimal embedding parameters of a signal , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[5]  Kenichi Saito,et al.  Power spectrum density of EEGs of sleeping epilepsy-prone El mice and their non-epileptic mother strain. , 2006, The journal of physiological sciences : JPS.

[6]  Metin Akay,et al.  Measurement and Quantification of Spatiotemporal Dynamics of Human Epileptic Seizures , 2000 .

[7]  Luis Diambra,et al.  Nonlinear models for detecting epileptic spikes , 1999 .

[8]  A. Walker.  Electroencephalography, Basic Principles, Clinical Applications and Related Fields , 1982 .

[9]  J. Jeong,et al.  Test for low-dimensional determinism in electroencephalograms. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[10]  Alexandre Andrade,et al.  Correlation Dimension Maps of EEG from Epileptic Absences , 1999, Brain Topography.

[11]  Gaoxiang Ouyang,et al.  Nonlinear similarity analysis for epileptic seizures prediction , 2006 .

[12]  Danilo P Mandic,et al.  Indications of nonlinear structures in brain electrical activity. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Montri Phothisonothai,et al.  Fractal-based EEG data analysis of body parts movement imagery tasks. , 2007, The journal of physiological sciences : JPS.

[14]  L. Cao.  Practical method for determining the minimum embedding dimension of a scalar time series , 1997 .

[15]  J. C. Sackellares,et al.  Measurement and Quantification of Spatio-Temporal Dynamics of Human Epileptic Seizures , 1999 .

[16]  N. Kannathal,et al.  Complex dynamics of epileptic EEG , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  N. McGrogan.  Neural network detection of epileptic seizures in the electroencephalogram , 2001 .

[18]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[19]  Martienssen,et al.  Characterization of spatiotemporal chaos from time series. , 1993, Physical review letters.