Dynamical Similarity Analysis of EEG Recordings

Electroencephalogram (EEG) recordings contain a large amount of information about physiological and pathological processes in the brain and serve as one of the important tools in clinical diagnosis and research regarding epilepsy. Dynamical similarity analysis is applied to characterize EEG changes in different absence seizure states. The average similarity measure of a pair of EEG signals in the same seizure states and across different seizure states is calculated using an improved dynamical similarity method. The results show that the average similarity measures between EEG segments within the seizure-free state are close to 1, suggesting that the EEG segments within the seizure-free state share the same dynamic characteristics. The similarity measures between EEG segments across different seizure states are typically smaller, indicating that the changes of dynamic characteristics can be found during different absence seizure states.

[1]  J. Martinerie,et al.  Seizure anticipation in human neocortical partial epilepsy. , 2002, Brain : a journal of neurology.

[2]  W. Stacey,et al.  Technology Insight: neuroengineering and epilepsy—designing devices for seizure control , 2008, Nature Clinical Practice Neurology.

[3]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[4]  Jürgen Kurths,et al.  Influence of observational noise on the recurrence quantification analysis , 2002 .

[5]  Thomas Schreiber,et al.  Classification of Time Series Data with Nonlinear Similarity Measures , 1997 .

[6]  Xiaoli Li,et al.  Dynamical characteristics of pre-epileptic seizures in rats with recurrence quantification analysis , 2004 .

[7]  I. Gath,et al.  On the prediction of epileptic seizures , 1981, Biological Cybernetics.

[8]  Mingzhou Ding,et al.  Estimating correlation dimension from a chaotic time series: when does plateau onset occur? , 1993 .

[9]  D. Ruelle,et al.  Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems , 1992 .

[10]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[11]  J. Martinerie,et al.  Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings. , 1999, Neuroreport.

[12]  L. Danober,et al.  Pathophysiological mechanisms of genetic absence epilepsy in the rat , 1998, Progress in Neurobiology.

[13]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[14]  F. H. Lopes da Silva,et al.  Model of brain rhythmic activity , 1974, Kybernetik.

[15]  Manish Sarkar,et al.  Characterization of medical time series using fuzzy similarity-based fractal dimensions , 2003, Artif. Intell. Medicine.

[16]  G. Buzsáki.  Rhythms of the brain , 2006 .

[17]  A. Selverston,et al.  Dynamical principles in neuroscience , 2006 .

[18]  C. Elger,et al.  CAN EPILEPTIC SEIZURES BE PREDICTED? EVIDENCE FROM NONLINEAR TIME SERIES ANALYSIS OF BRAIN ELECTRICAL ACTIVITY , 1998 .

[19]  Schreiber,et al.  Quantification of depth of anesthesia by nonlinear time series analysis of brain electrical activity , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[20]  D. Gribkov,et al.  Learning dynamics from nonstationary time series: analysis of electroencephalograms. , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[21]  Ben H. Jansen,et al.  Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns , 1995, Biological Cybernetics.

[22]  Fabrice Wendling,et al.  Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals , 2000, Biological Cybernetics.

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

[24]  Cornelis J Stam,et al.  Nonlinear EEG analysis during sleep in premature and full-term newborns , 2003, Clinical Neurophysiology.

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

[26]  J. Salas,et al.  Nonlinear dynamics, delay times, and embedding windows , 1999 .

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

[28]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.