A Comparison between the Use of ESNN on Long Stereo-EEG Recordings and Their Largest Lyapunov Exponent Profiles for Epileptic Brain Analysis

In the last 25 years many works in literature about the capability to detect or predict the occurrence of epileptic seizures, starting from the electroencephalogram (EEG) signal analysis, have often hypothesized that the epileptogenic activity is the result of an abnormal electrical activity hyper-synchronization of different points in an epileptic brain. We already proposed our method to integrate Neural Networks (NN) and the largest Lyapunov exponent (Lmax) for capturing brain dynamics through long stereo-EEG (sEEG) data recorded. In this paper we want to compare the use of a classical Evolving Spiking NN (ESNN) on long sEEG recordings with the integrated method previously proposed. Results are interesting and encourage us to develop, in the next future, a framework for EEG signal analysis.

[1]  W. Art Chaovalitwongse,et al.  Adaptive epileptic seizure prediction system , 2003, IEEE Transactions on Biomedical Engineering.

[2]  Hojjat Adeli,et al.  A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection , 2009, Neural Networks.

[3]  Nikola K. Kasabov,et al.  NeuCube EvoSpike Architecture for Spatio-temporal Modelling and Pattern Recognition of Brain Signals , 2012, ANNPR.

[4]  I. Osorio,et al.  Controlled test for predictive power of Lyapunov exponents: their inability to predict epileptic seizures. , 2004, Chaos.

[5]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

[6]  M. Rosenstein,et al.  A practical method for calculating largest Lyapunov exponents from small data sets , 1993 .

[7]  Ying-Cheng Lai,et al.  Inability of Lyapunov exponents to predict epileptic seizures. , 2003, Physical review letters.

[8]  W. J. Williams,et al.  Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures , 2005, Brain Topography.

[9]  P. Pardalos,et al.  An investigation of EEG dynamics in an animal model of temporal lobe epilepsy using the maximum Lyapunov exponent , 2009, Experimental Neurology.

[10]  Nikola K. Kasabov,et al.  Evolving spiking neural networks for spatio-and spectro-temporal pattern recognition , 2012, 2012 6th IEEE International Conference Intelligent Systems.

[11]  H. Kantz.  A robust method to estimate the maximal Lyapunov exponent of a time series , 1994 .

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

[13]  Nikola Kasabov,et al.  Evolving Connectionist Systems: The Knowledge Engineering Approach , 2007 .

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

[15]  Stefan Schliebs,et al.  Constructing Robust Liquid State Machines to Process Highly Variable Data Streams , 2012, ICANN.

[16]  Filip Ponulak,et al.  Introduction to spiking neural networks: Information processing, learning and applications. , 2011, Acta neurobiologiae experimentalis.

[17]  Lino Nobili,et al.  Integrating Neural Networks and Chaotic Measurements for Modelling Epileptic Brain , 2012, ICANN.

[18]  Nicholas T. Carnevale,et al.  Simulation of networks of spiking neurons: A review of tools and strategies , 2006, Journal of Computational Neuroscience.

[19]  F. Mormann,et al.  Seizure prediction: the long and winding road. , 2007, Brain : a journal of neurology.