Optimal Classification of Epileptic EEG Signals Using Neural Networks and Harmony Search Methods

In this paper, the Harmony Search (HS)-aided BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Three HS methods, the original version and two new variations recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training.

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

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

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

[4]  Xiaolei Wang,et al.  A hybrid optimization method of harmony search and opposition-based learning , 2012 .

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

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

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

[8]  Xiaofeng Wang,et al.  A bee foraging-based memetic Harmony Search method , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[9]  Riccardo Poli,et al.  Foundations of Genetic Programming , 1999, Springer Berlin Heidelberg.

[10]  Jing Wang,et al.  BP Neural Networks with Harmony Search Method-based Training for Epileptic EEG Signal Classification , 2012, 2012 Eighth International Conference on Computational Intelligence and Security.

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

[12]  Carlos Cotta,et al.  Memetic algorithms and memetic computing optimization: A literature review , 2012, Swarm Evol. Comput..

[13]  Athanasios V. Vasilakos,et al.  ASAFES2: a novel, neuro-fuzzy architecture for fuzzy computing, based on functional reasoning , 1996, Fuzzy Sets Syst..

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

[15]  Xiaofeng Wang,et al.  A novel Harmony Search method with dual memory , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[16]  Hamid R. Tizhoosh,et al.  Opposition-Based Reinforcement Learning , 2006, J. Adv. Comput. Intell. Intell. Informatics.

[17]  Mohammed Azmi Al-Betar,et al.  University Course Timetabling Using a Hybrid Harmony Search Metaheuristic Algorithm , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[19]  Duc Truong Pham,et al.  The Bees Algorithm: Modelling foraging behaviour to solve continuous optimization problems , 2009 .

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

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

[22]  Antero Arkkio,et al.  A hybrid optimization method for wind generator design , 2012 .

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

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

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

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

[27]  Bin Wu,et al.  Hybrid harmony search and artificial bee colony algorithm for global optimization problems , 2012, Comput. Math. Appl..

[28]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[29]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

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

[31]  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.

[32]  Jing Wang,et al.  Epileptic EEG signal classification with marching pursuit based on harmony search method , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

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

[34]  Khang Nguyen,et al.  An hybrid algorithm of Harmony Search and Bees Algorithm for a University Course Timetabling Problem , 2012 .

[35]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[36]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[37]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[38]  Shahryar Rahnamayan,et al.  A novel population initialization method for accelerating evolutionary algorithms , 2007, Comput. Math. Appl..

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

[40]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..