Classification of Epileptic EEG Data Using Multidimensional Scaling

A methodology is proposed and developed for epileptic seizures prediction through multifeatures extracted from EEG, and submitted to space reduction. Concepts from energy, frequency-time, and nonlinear dynamics are used to obtain the set of 14 features. Multidimensional scaling is used for space reduction from high dimensional to three dimensional space, in the VISRED platform. Results show the potentiality of this methodology. A computational system with an algorithms base and a data base, under development, is briefly sketched, in order to allow to face the high variability of biological systems.

[1]  P. Pardalos,et al.  Performance of a seizure warning algorithm based on the dynamics of intracranial EEG , 2005, Epilepsy Research.

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

[3]  Patrick J. F. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 2003 .

[4]  Jorge Henriques,et al.  Iterative Multidimensional Scaling for Industrial Process Monitoring , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[5]  A. Schulze-Bonhage,et al.  Seizure prediction: The impact of long prediction horizons , 2007, Epilepsy Research.

[6]  Brian Litt,et al.  Continuous energy variation during the seizure cycle: towards an on-line accumulated energy , 2005, Clinical Neurophysiology.

[7]  Gregory L. Holmes,et al.  Handbook of Epilepsy , 1997 .

[8]  P. Groenen,et al.  Modern multidimensional scaling , 1996 .

[9]  G. Vachtsevanos,et al.  Epileptic Seizures May Begin Hours in Advance of Clinical Onset A Report of Five Patients , 2001, Neuron.

[10]  Paulo Barbeiro,et al.  VISRED -Numerical Data Mining with Linear and Nonlinear Techniques , 2007, Industrial Conference on Data Mining.

[11]  Wojciech Basalaj.  Proximity visualisation of abstract data , 2001 .

[12]  S. Kochen,et al.  Prediction of epileptic seizures using accumulated energy in a multiresolution framework , 2004, Journal of Neuroscience Methods.

[13]  Michel Le Van Quyen,et al.  Exploring the dynamics of collective synchronizations in large ensembles of brain signals , 2006, Journal of Physiology-Paris.