Random Sampling in the Detection of Epileptic EEG Signals

The detection of epileptic EEG signals is a challenging task due to bulky size and nonstationary nature of the data. From a pattern recognition point of view, one key problem is how to represent the large amount of recorded EEG signals for further analysis such as classification.This chapter introduces a new classification algorithm combining a simple random sampling (SRS) technique and a least square support vector machine (LS-SVM) to identify epilptic seizure from two-class EEG signals.

[1]  Amitava Chatterjee,et al.  Cross-correlation aided support vector machine classifier for classification of EEG signals , 2009, Expert Syst. Appl..

[2]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[3]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[4]  Yan Li,et al.  EEG signal classification based on simple random sampling technique with least square support vector machine , 2011 .

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

[6]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[7]  Yan Li,et al.  A novel statistical algorithm for multiclass EEG signal classification , 2014, Eng. Appl. Artif. Intell..

[8]  Yan Li,et al.  Classification of EEG Signals Using Sampling Techniques and Least Square Support Vector Machines , 2009, RSKT.

[9]  X. C. Guo,et al.  PSO-Based Hyper-Parameters Selection for LS-SVM Classifiers , 2006, ICONIP.

[10]  Elif Derya íbeyli.  Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals , 2010 .

[11]  Karthik Suresh,et al.  Design, data analysis and sampling techniques for clinical research , 2011, Annals of Indian Academy of Neurology.

[12]  Davut Hanbay.  An expert system based on least square support vector machines for diagnosis of the valvular heart disease , 2009, Expert Syst. Appl..

[13]  E. Reynolds,et al.  The ILAE/IBE/WHO Global Campaign against Epilepsy: Bringing Epilepsy “Out of the Shadows” , 2000, Epilepsy & Behavior.

[14]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Elif Derya íbeyli.  Wavelet/mixture of experts network structure for EEG signals classification , 2008 .