Measurement and Quantification of Spatiotemporal Dynamics of Human Epileptic Seizures

Since its discovery by Hans Berger in 1929, the electroencephalogram (EEG) has been the most utilized signal to clinically assess brain function. The enormous complexity of the EEG signal, both in time and space, should not surprise us since the EEG is a direct correlate of brain function. If the system to be probed is complex and our signal is a reliable descriptor of its function, then we can also expect complexity in the signal. Unfortunately, traditional signal processing (SP) theory is based on very simple assumptions about the system that produced the signal (e.g. linearity assumption). Hence the application of SP methodologies to automatically quantify the EEG has met the challenge with varying degrees of success. We can say that today the trained electroencephalographer or neurologist are still the golden standards in characterizing phasic events in the EEG such as spikes, the quantification of background activity (as in sleep staging) and identification and localization of epileptic seizures.

[1]  J. S. Barlow,et al.  Methods of Analysis of Nonstationary EEGs, with Emphasis on Segmentation Techniques: A Comparative Review , 1985, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[2]  Leonidas D. Iasemidis,et al.  Characterizing nonlinearity in invasive EEG recordings from temporal lobe epilepsy , 1996 .

[3]  Walter J. Freeman,et al.  Strange attractors that govern mammalian brain dynamics shown by trajectories of electroencephalographic (EEG) potential , 1988 .

[4]  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.

[5]  M. Paluš,et al.  Information theoretic test for nonlinearity in time series , 1993 .

[6]  Y. Pesin.  CHARACTERISTIC LYAPUNOV EXPONENTS AND SMOOTH ERGODIC THEORY , 1977 .

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

[8]  G. Ferber,et al.  Treatment of some nonstationarities in the EEG. , 1987, Neuropsychobiology.

[9]  Henry D. I. Abarbanel,et al.  Analysis of Observed Chaotic Data , 1995 .

[10]  J. Príncipe,et al.  Chaotic activity during iron-induced 'epileptiform' discharge in rat hippocampal slices , 1992, IEEE Transactions on Biomedical Engineering.

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

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

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

[14]  G. Pfurtscheller.  Handbook of electroencephalography and clinical neurophysiology , 1978 .

[15]  Leonidas D. Iasemidis,et al.  On the dynamics of the human brain in temporal lobe epilepsy. , 1991 .

[16]  H. Haken.  Principles of brain functioning , 1995 .

[17]  A. Wolf,et al.  Intermediate Length Scale Effects in Lyapunov Exponent Estimation , 1986 .

[18]  E. J. Kostelich,et al.  Comparison of Algorithms for Determining Lyapunov Exponents from Experimental Data , 1986 .

[19]  P. Walters.  Introduction to Ergodic Theory , 1977 .

[20]  J. E. Skinner,et al.  Chaos and physiology: deterministic chaos in excitable cell assemblies. , 1994, Physiological reviews.

[21]  B. Jansen,et al.  Structural EEG analysis: an explorative study. , 1988, International journal of bio-medical computing.

[22]  Leonidas D. Iasemidis,et al.  Spatiotemporal dynamics of human epileptic seizures , 1996 .

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

[24]  Eric J. Kostelich,et al.  Problems in estimating dynamics from data , 1992 .

[25]  Gregory W. Wornell,et al.  Signal processing in the context of chaotic signals , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[26]  W. J. Williams,et al.  Phase space analysis of EEG in temporal lobe epilepsy , 1988, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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