Single-class SVM and directed transfer function approach to the localization of the region containing epileptic focus

The paper presents the study concerning the application of a single-class support vector machine (SVM) and directed transfer function method for the localization of the region of the brain containing the epileptic focus on the basis of EEG registration. The results of the performed numerical experiments for the localization of the seizure focus in the brain will be demonstrated on the examples of EEG for few patients.

[1]  Wim Van Paesschen,et al.  Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram , 2006, IEEE Transactions on Biomedical Engineering.

[2]  Mingzhou Ding,et al.  Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance , 2001, Biological Cybernetics.

[3]  Xuelong Li,et al.  Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Bin He,et al.  Spatio-temporal EEG source localization using a three-dimensional subspace FINE approach in a realistic geometry inhomogeneous head model , 2006, IEEE Transactions on Biomedical Engineering.

[5]  H. T. Ellis,et al.  Lidar observations of a stratospheric dust cloud layer in the tropics , 1975 .

[6]  Athina P. Petropulu,et al.  Higher-order spectral analysis , 2006 .

[7]  Andrzej Cichocki,et al.  Multichannel EEG brain activity pattern analysis in time–frequency domain with nonnegative matrix factorization support , 2007 .

[8]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[9]  Saeid Sanei,et al.  Epileptic seizure predictability from scalp EEG incorporating constrained blind source separation , 2006, IEEE Transactions on Biomedical Engineering.

[10]  Leon D. Iasemidis,et al.  Epileptic seizure prediction and control , 2003, IEEE Transactions on Biomedical Engineering.

[11]  Patrick Dupont,et al.  Canonical decomposition of ictal scalp EEG reliably detects the seizure onset zone , 2007, NeuroImage.

[12]  D. Sandwell BIHARMONIC SPLINE INTERPOLATION OF GEOS-3 AND SEASAT ALTIMETER DATA , 1987 .

[13]  Stanislaw Osowski,et al.  Epileptic seizure characterization by Lyapunov exponent of EEG signal , 2007 .

[14]  P J Franaszczuk,et al.  Analysis of mesial temporal seizure onset and propagation using the directed transfer function method. , 1994, Electroencephalography and clinical neurophysiology.

[15]  J. Gotman Measurement of small time differences between EEG channels: method and application to epileptic seizure propagation. , 1983, Electroencephalography and clinical neurophysiology.

[16]  Angel R. Martinez,et al.  MATLAB Statistics Toolbox , 2001 .

[17]  Katarzyna J. Blinowska,et al.  A new method of the description of the information flow in the brain structures , 1991, Biological Cybernetics.

[18]  V. Susić,et al.  Metaphit‐Induced Audiogenic Seizure and Its Inhibition by MK‐801: Electroencephalographic and Behavioral Characterization , 1993, Epilepsia.

[19]  H. M. Weinmann,et al.  Handbook of EEG and clinical neurophysiology , 1978 .

[20]  G. Wang,et al.  Directed coherence as a measure of interhemispheric correlation of EEG. , 1992, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[21]  Rasmus Bro,et al.  Multiway analysis of epilepsy tensors , 2007, ISMB/ECCB.

[22]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[23]  Chrysostomos L. Nikias,et al.  Higher-order spectral analysis , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.