Nesibe YALÇIN, Gülay TEZEL, Cihan KARAKUZU

Epilepsy diagnosis using artificial neural network learned by PSO

Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG records. For this purpose, an artificial neural network (ANN), widely used and known as an active classification technique, is applied. The particle swarm optimization (PSO) method, which does not need gradient calculation, derivative information, or any solution of differential equations, is preferred as the training algorithm for the ANN. A PSO-based neural network (PSONN) model is diversified according to PSO versions, and 7 PSO-based neural network models are described. Among these models, PSONN3 and PSONN4 are determined to be appropriate models for epilepsy diagnosis due to having better classification accuracy. The training methods-based PSO versions are compared with the backpropagation algorithm, which is a traditional method. In addition, different numbers of neurons, iterations/generations, and swarm sizes have been considered and tried. Results obtained from the models are evaluated, interpreted, and compared with the results of earlier works done with the same dataset in the literature.

Anahtar Kelimeler:

Artificial neural networks, backpropagation algorithm, electroencephalogram, epilepsy diagnosis, particle swarm optimization

Epilepsy diagnosis using artificial neural network learned by PSO

Keywords:

Artificial neural networks, backpropagation algorithm, electroencephalogram, epilepsy diagnosis, particle swarm optimization,

PDF

___

E. Yazgan, M. Kor¨urek, Medikal Elektronik, ˙Istanbul, Turkey, ˙ITU, 1996 (in Turkish).
E. Sezer, “EEG signal analysis for epilepsy diagnosis”, MSc, Sel¸cuk University, Konya, Turkey, 2008 (in Turkish with English abstract).
M.A. C¸ avu¸slu, C. Karakuzu, F. Karakaya, “Neural identification of dynamic systems on FPGA with improved PSO learning”, Applied Soft Computing, Vol. 12, pp. 2707–2718, 2012.
M. Akın, M.A. Arserim, M.K. Kıymık, ˙I. T¨urko˘glu, “A new approach for diagnosing epilepsy by using wavelet transform and neural networks”, Proceedings of the 23rd Annual EMBS International Conference, ˙Istanbul, Turkey, pp. 1596–1599, 2001.
A. Suba¸sı, A. Alkan, E. K¨okl¨ukaya, “Wavelet neural network classification of EEG signals”, Teknoloji, Vol. 7, pp. 71–80, 2004 (in Turkish with English abstract).
N. Kannathal, M.L. Choo, U.R. Acharya, P.K. Sadasivan, “Entropies for detection of epilepsy in EEG”, Computer Methods and Programs in Biomedicine, Vol. 80, pp. 187–194, 2005.
˙I. G¨uler, E.D. ¨Ubeyli, “Multiclass support vector machines for EEG-signals classification”, IEEE Transactions on Information Technology in Biomedicine, Vol. 11, pp. 117–126, 2007.
K. Polat, S. G¨une¸s, “Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform”, Applied Mathematics and Computation, Vol. 187, pp. 1017–1026, 2007.
A. Suba¸sı, “EEG signal classification using wavelet feature extraction and a mixture of expert model”, Expert Systems with Applications, Vol. 32, pp. 1084–1093, 2007.
A.T. Tzallas, M.G. Tsipouras, D.I. Fotiadis, “A time-frequency based method for the detection of epileptic seizures in EEG recordings”, Computer-Based Medical Systems Symposium, pp. 135–140, 2007.
C.R. Hema, M.P. Paulraj, R. Nagarajan, S. Yaacob, A.H. Adom, “Application of particle swarm optimization for EEG signal classification”, Biomedical Soft Computing and Human Sciences, Vol. 13, pp. 79–84, 2008.
L. Guo, D. Rivero, J.A. Seoane, A. Pazos, “Classification of EEG signals using relative wavelet energy and artificial neural networks”, Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 177–183, 2009.
G. Tezel, Y. ¨Ozbay, “A new approach for epileptic seizure detection using adaptive neural network”, Expert Systems with Applications, Vol. 36, pp. 172–180, 2009.
D. Wang, D. Miao, C. Xie, “Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection”, Expert Systems with Applications, Vol. 38, pp. 14314–14320, 2011.
R.G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, C.E. Elger, “Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: dependence on recording region and brain state”, Physical Review E, Vol. 64, p. 061907, 2001.
D. Rumelhart, J. McClelland, Parallel Distributed Processing, Cambridge, MA, USA, MIT Press, 1986.
J. Kennedy, R.C. Eberhart, “Particle swarm optimization”, Proceedings of the IEEE International Conference on Neural Networks, Vol. 4, pp. 1942–1948, 1995.
S. Ba˘gı¸s, “System modeling by using artificial intelligence algorithms”, MSc, Erciyes University, Kayseri, Turkey, 2009 (in Turkish with English abstract).
L. Liu, W. Liu, D.A. Cartes, “Particle swarm optimization based parameter identification applied to permanent magnet synchronous motors”, Engineering Applications of Artificial Intelligence, Vol. 21, pp. 1092–1100, 2008.
N. Barı¸s¸cı, S. M¨uld¨ur, “Classification of epileptic EEG signals using neuro-fuzzy system”, Journal of Polytechnic, Vol. 6, pp. 445–449, 2003 (in Turkish with English abstract).
N. Yal¸cın, G. Tezel, C. Karakuzu, “Epilepsy diagnosis using artificial neural network learned by PSO”, 9th International Conference on Electronics, Computer and Computation (ICECCO ‘12), pp. 37–41, 2012.
N. Yal¸cın, “Heuristic algorithm basis artificial neural networks for epilepsy detection”, MSc, Sel¸cuk University, Konya, Turkey, 2012 (in Turkish with English abstract).
Y. Shi, R.C. Eberhart, “Empirical study of particle swarm optimization”, Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1945–1950, 1999.
R.C. Eberhart, Y. Shi, “Comparing inertia weights and constriction factors in particle swarm optimization”, Proceedings of the Congress on Evolutionary Computation, pp. 84–88, 2000.
H.J. Park, D.U. Jeong, K.S. Park, “Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method”, IEEE Transactions on Biomedical Engineering, Vol. 49, pp. 1526–1533, 200
M. S¸evkli, M.M. Yenisey, “A particle swarm optimization for the job shop scheduling problems”, It¨u Der- gisi/M¨uhendislik, Vol. 5, pp. 58–68, 2006 (in Turkish with English abstract).
Z. Wang, X. Sun, D. Zhang, “A PSO-based classification rule mining algorithm”, Lecture Notes in Computer Science, Vol. 4682, pp. 377–384, 2007.
A. ¨Ozbeyaz, “LS-SVM hyper parameter selection based on PSO in EMG and EEG signals classification”, MSc, Kahramanmara¸s S¨ut¸c¨u Imam University, Kahramanmara¸s, Turkey, 2010 (in Turkish with English abstract).