Modeling and analyzing non-seizure EEG data for patients with epilepsy

We present nonlinear analysis of non-seizure electroencephalogram (EEG) time series data from four epileptic patients. A non-seizure state is a period that is free of any part of an epileptic seizure, including the transition to a fully developed episode. EEG measurements are typically contaminated with a large amount of non- neurophysiological source information, generally called artifact, which arises, for example, from eye movement, muscle tension, and physical motion. The first objective of this study is to gain some insight into how much variability in analysis results to be expected from patients having similar clinical characteristics. The second objective is to investigate the impact of eye movement on the analysis results. A special feature presented here is the introduction and testing of a filter for eye movement artifact. The third objective is to determine if neurophysiological activity as viewed from two adjacent channels appears dynamically to be the same.

[1]  P. Grassberger,et al.  Characterization of experimental (noisy) strange attractors , 1984 .

[2]  A. Fraser Reconstructing attractors from scalar time series: A comparison of singular system and redundancy criteria , 1989 .

[3]  N. E. Clapp,et al.  Nonlinear analysis of EEG for epileptic seizures , 1995 .

[4]  F. Takens Detecting strange attractors in turbulence , 1981 .

[5]  C. K. Yuen,et al.  Theory and Application of Digital Signal Processing , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Daw,et al.  Role of low-pass filtering in the process of attractor reconstruction from experimental chaotic time series. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[7]  C. J. Stam,et al.  Investigation of nonlinear structure in multichannel EEG , 1995 .

[8]  T. Teichmann,et al.  The Measurement of Power Spectra , 1960 .

[9]  Erol Baş,et al.  Chaos in Brain Function , 1990, Springer Berlin Heidelberg.

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

[11]  H. Herzel Chaotic Evolution and Strange Attractors , 1991 .

[12]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[13]  James Theiler,et al.  On the evidence for how-dimensional chaos in an epileptic electroencephalogram , 1995 .

[14]  D. Ruelle,et al.  Ergodic theory of chaos and strange attractors , 1985 .

[15]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

[16]  L. Tsimring,et al.  The analysis of observed chaotic data in physical systems , 1993 .

[17]  A. Babloyantz,et al.  Low-dimensional chaos in an instance of epilepsy. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[18]  P. Grassberger,et al.  NONLINEAR TIME SEQUENCE ANALYSIS , 1991 .