Epileptic EEG signal classification with marching pursuit based on harmony search method

In Epilepsy EEG signal classification, the main time-frequency features can be extracted by using sparse representation with marching pursuit (MP) algorithm. However, the computational burden is so heavy that it is almost impossible to apply MP to real time signal processing. To reduce complexity of sparse representation, we propose to adopt harmony search method in searching the best atoms. Because harmony search method can find the best atoms in continuous time-frequency dictionary, the performance of epilepsy EEG signal classification is enhanced. The validity of this method is proved by experimental results.

[1]  Zhifeng Zhang,et al.  Adaptive time-frequency decompositions , 1994 .

[2]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[3]  Elif Derya Übeyli.  Combined neural network model employing wavelet coefficients for EEG signals classification , 2009, Digit. Signal Process..

[4]  Daniel Graupe,et al.  A neural-network-based detection of epilepsy , 2004, Neurological research.

[5]  Elif Derya Übeyli,et al.  Recurrent neural networks employing Lyapunov exponents for EEG signals classification , 2005, Expert Syst. Appl..

[6]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[7]  Jing Wang,et al.  Epileptic Electroencephalogram Signal Classification based on Sparse Representation , 2011, IJCCI.

[8]  Zhang Chunmei,et al.  Signal overcomplete representation and sparse decomposition based on redundant dictionaries , 2005 .

[9]  Elif Derya íbeyli.  Lyapunov exponents/probabilistic neural networks for analysis of EEG signals , 2010 .

[10]  Chunmei Zhang.  Signal overcomplete Signal overcomplete decomposition based on redundant dictionaries , 2005 .

[11]  Elif Derya Übeyli.  Lyapunov exponents/probabilistic neural networks for analysis of EEG signals , 2010, Expert Syst. Appl..

[12]  Abdulhamit Subasi,et al.  Epileptic seizure detection using dynamic wavelet network , 2005, Expert Syst. Appl..

[13]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[14]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[15]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[16]  Abdulhamit Subasi.  Automatic detection of epileptic seizure using dynamic fuzzy neural networks , 2006, Expert Syst. Appl..

[17]  Daniel Rivero,et al.  Automatic feature extraction using genetic programming: An application to epileptic EEG classification , 2011, Expert Syst. Appl..

[18]  Abdulhamit Subasi,et al.  Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction , 2007, Comput. Biol. Medicine.

[19]  Aswin C. Sankaranarayanan,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[20]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[21]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[22]  Abdulhamit Subasi,et al.  Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients , 2005, Expert Syst. Appl..

[23]  Hasan Ocak,et al.  Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy , 2009, Expert Syst. Appl..

[24]  Abdulhamit Subasi,et al.  EEG signal classification using wavelet feature extraction and a mixture of expert model , 2007, Expert Syst. Appl..

[25]  V. Srinivasan,et al.  Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features , 2005, Journal of Medical Systems.

[26]  Guillermo Sapiro,et al.  Online dictionary learning for sparse coding , 2009, ICML '09.

[27]  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.