Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier

Authors: I. Omerhodzic, S. Avdakovic, A. Nuhanovic, K. Dizdarevic

Abstract:

In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (δ, θ, α, β and γ) and the Parseval-s theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.

Keywords: Epilepsy, EEG, Wavelet transform, Energydistribution, Neural Network, Classification.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058049

References:

[1] S. Tong and N.V.Thacor, Engineering in Medicine & Biology- Quantitative EEG Analysis Methods and Clinical Applications, Boston/London: Artech House, 2009.
[2] L. M. Patnaika and O. K. Manyamb, "Epileptic EEG detection using neural networks and post-classification" Computer methods and programs in biomedicine- Elsevier, vol. 91, pp. 100-109. 2008.
[3] A. Prochazka, J. Kukal and O.Vysata, "Wavelet transform use for feature extraction and EEG signal segments classification," in Proc. 2008 IEEE Communications, Control and Signal Processing, 3rd International Symposium, pp. 719 - 722.
[4] V. Bostanov and B. Kotchoubey, "Recognition of affective prosody: Continuous wavelet measures of event-related brain potentials to emotional exclamations," Psychophysiology, vol. 41, pp. 259-268, 2004.
[5] M. Murugappan, M. Rizon, R. Nagarajan, S. Yaacob, I. Zunaidi, and D. Hazry, "EEG Feature Extraction for Classifying Emotions using FCM and FKM," Int. Journal of Computers and Comunications, vol. 1, pp. 21-25, 2007.
[6] C. Wang, J. Zou, J. Zhang, M. Wang and R. Wang, "Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn", Biomedical and Life Sciences- Springer, vol. 4, pp. 233-240, 2010.
[7] L. Guo, D. Rivero, J. A. Seoane and A. Pazos, "Classification of EEG Signals Using Relative Wavelet Energy and Artificial Neural Networks," GEC-09, Shanghai, China, 2009.
[8] M. Akin, M. A. Arserim, M. K. Kiymik and I. Turkoglu, "A New Approach For Diagnosing Epilepsy By Using Wavelet Transform And Neural Networks," in Proc. 2001 IEEE/EMBS 23rd Annual Conference, pp. 1596 - 1599.
[9] H. S. Liu, T. Zhang and F. S. Yang, "A Multistage, Multimethod Approach for Automatic Detection and Classification of Epileptiform EEG," IEEE Transactions on Biomedical Engineering, vol. 49, pp. 1557 - 1566, 2002.
[10] P. Jahankhani, V. Kodogiannis, and K. Revett, "EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks," in Proc. 2003 International Symposium on Modern Computing, pp. 120 - 124.
[11] A. A. Mashakbeh, "Analysis Of Electroencephalogram To Detect Epilepsy," Int. Journal Of Academic Research, vol. 2, pp. 63-69, 2010.
[12] M. M. Shaker, "EEG Waves Classifier using Wavelet Transform and Fourier Transform," Int. Journal of Biological and Life Sciences, vol. 1, pp. 85-90, 2005.
[13] I. Omerhodzic, E. Causevic, K. Dizdarevic, S. Avdakovic, M. Music, M. Kusljugic, E. Hajdarpasic, N. Kadic, "First neurosurgical experience with the wavelet based EEG in diagnostic of concussion," 11th Congress of Neurosurgeons of Serbia, Nis, Serbia, 2008. Abstract book p. 18.
[14] L. M. Patnaik, O. K. Manyam, "Epileptic EEG detection using neural networks and post-classification". Comput Methods Programs Biomed. 2008 Aug;91(2):100-9.
[15] A. Subasi, E. Erçelebi, "Classification of EEG signals using neural network and logistic regression". Comput Methods Programs Biomed. 2005 May;78(2):87-99.
[16] K. Asaduzzaman, M. B. Reaz, F. Mohd-Yasin, K. S. Sim, M. S. Hussain, "A study on discrete wavelet-based noise removal from EEG signals", Adv Exp Med Biol. 2010;680:593-9.
[17] H. Adeli, Z. Zhou, N. Dadmehr. "Analysis of EEG records in an epileptic patient using wavelet transform", J Neurosci Methods. 2003 Feb 15;123(1):69-87.
[18] L. Guo, D. Rivero, J. Dorado, J. R. Rabunal, A. Pazos, "Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks", J Neurosci Methods. 2010 Aug 15;191(1):101-9.
[19] A. S. Zandi, M. Javidan, G. A. Dumont, R. Tafreshi, "Automated realtime epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform", IEEE Trans Biomed Eng. 2010 Jul;57(7):1639-51.
[20] P. Mirowski, D. Madhavan, Y. Lecun, R. Kuzniecky, Classification of patterns of EEG synchronization for seizure prediction. Clin Neurophysiol. 2009 Nov;120(11):1927-40.
[21] A. S. Zandi, G. A. Dumont, M. Javidan, R. Tafreshi, B. A. MacLeod, C. R. Ries, E. A. Puil, "A novel wavelet-based index to detect epileptic seizures using scalp EEG signals", Conf Proc IEEE Eng Med Biol Soc. 2008;2008:919-22.
[22] A. Subasi, A. Alkan, E. Koklukaya, M. K. Kiymik, "Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing", Neural Netw. Sep 2005, vol. 18(7), 985-997.
[23] S. J. Schiff, A. Aldroubi, M. Unser, S. Sato, "Fast wavelet transformation of EEG", Electroencephalogr Clin Neurophysiol. Dec 1994, vol. 91(6), 442-455.
[24] L. Senhadji, J. L. Dillenseger, F. Wendling, C. Rocha, A. Kinie, "Wavelet analysis of EEG for three-dimensional mapping of epileptic events", Ann Biomed Eng. Sep-Oct 1995, 23(5):543-52.
[25] C. E. D'Attellis, S. I. Isaacson, R. O. Sirne, "Detection of epileptic events in electroencephalograms using wavelet analysis", Ann Biomed Eng. Mar-Apr 1997, 25(2):286-93.
[26] H. Adeli, S. Ghosh-Dastidar, N. Dadmehr, "A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy", IEEE Trans Biomed Eng. Feb 2007, 54(2):205-11.
[27] H. Leung, K. Schindler, A. Y. Chan, A. Y. Lau, K. L. Leung, E. H. Ng, K. S. Wong, "Wavelet-denoising of electroencephalogram and the absolute slope method: a new tool to improve electroencephalographic localization and lateralization", Clin Neurophysiol. Jul 2009, 120(7):1273-81.
[28] I. Daubechies, Ten Lectures on Wavelets, Philadelphia: Society for Industrial and Applied Mathematics, 1992.
[29] H. He and J. A. Starzyk, "A Self-Organizing Learning Array System for Power Quality Classification Based on Wavelet Transform," IEEE Transaction On Power Delivery, vol. 21(1), pp. 286-295, 2006.
[30] S. Mallat, A Wavelet Tour of Signal Processing, San Diego, CA: Academic, 1998.
[31] S. Avdakovic and A. Nuhanovic, "Identifications and Monitoring of Power System Dynamics Based on the PMUs and Wavelet Technique," International Journal of Energy and Power Engineering, vol. 3, pp. 202- 209, 2010.
[32] R. G. Andrzejak, K. Lehnertz, C. Rieke, F. Mormann, 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, Phys. Rev. E, 64, 061907.
[Online] Available: http://epileptologie-bonn.de/cms/
[33] P. Settipalli, "Automated Classification Of Power Quality Disturbances Using Signal Processing Technique and Neural Network", Ph.D. dissertation, University of Kentucky; USA, 2007.
[34] S. Dreiseitl, L. Ohno-Machado, Logistic regression and artificial neural network classification models: a methodology review, J. Biomed. Inform. vol. 35, 352-359, 2002.
[35] I.A. Basheer, M. Hajmeer, Artificial neural networks: fundamentals, computing, design, and application, J. Microbiol. Methods vol. 43, 3-31, 2000.
[36] B.B. Chaudhuri, U. Bhattacharya, Efficient training and improved performance of multilayer perceptron in pattern classification, Neurocomputing vol. 34, 11-27, 2000.