Abstract
The diagnosis of epilepsy from EEG signals by a human scorer is a very time consuming and costly task considering the large number of epileptic patients admitted to the hospitals and the large amount of data needs to be scored. In this paper, a hybrid method called adaptive particle swarm negative selection (APSNS) was introduced to automate the process of epileptic seizures detection in EEG signals. In the proposed method, an adaptive negative selection creates a set of artificial lymphocytes (ALCs) that are tolerant to normal patterns. However, the particle swarm optimization (PSO) algorithm forces these ALCs to explore the space of epileptic signals and maintain diversity and generality among them. The EEG signals were analyzed using discrete wavelet transform (DWT) to extract the most important information needed for decision making. The features extracted have been used to investigate the performance of the proposed APSNS algorithm in classifying the EEG signals. The Experimental results confirm effectiveness and stability of the proposed method. Its classification accuracy outperforms many of the methods in the literature.
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References
Hapuarachchi, P.: Feature selection and artifact removal in sleep stage classification. Master Thesis, University of Waterloo. Canada (2006)
Adeli, H., Zhou, Z., Dadmehr, N.: Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods 123, 69–87 (2003)
Übeyli, E.D.: Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals. Expert Systems with Applications 37, 233–239 (2010)
Subasi, A.: EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications 32, 1084–1093 (2007)
Nigam, V.P., Graupe, D.: A neural-network-based detection of epilepsy. Neurological Research 26, 55–60 (2004)
Ocak, H.: Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Signal Processing 88, 1858–1867 (2008)
Patnaik, L.M., Manyam, O.K.: Epileptic EEG detection using neural networks and post-classification. Computer Methods and Programs in Biomedicine 91, 100–109 (2008)
Gardner, A.B.: A novelty detection approach to seizure analysis from intracranial EEG. PhD Thesis, Georgia Institute of Technology. Georgia, United States (2004)
Subasi, A.: Automatic detection of epileptic seizure using dynamic fuzzy neural networks. Expert Systems with Applications 31, 320–328 (2006)
Subasi, A.: Epileptic seizure detection using dynamic wavelet network. Expert Systems with Applications 29, 343–355 (2005)
Kumar, S.P., Sriraam, N., Benakop, P.G., Jinaga, B.C.: Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Systems with Applications 37, 3284–3291 (2010)
Güler, İ., Übeyli, E.D.: Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. Journal of Neuroscience Methods 148, 113–121 (2005)
Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.K.: Entropies for detection of epilepsy in EEG. Computer Methods and Programs in Biomedicine 80, 187–194 (2005)
Übeyli, E.D.: Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents. Expert Systems with Applications 36, 9031–9038 (2009)
Acır, N., Güzeliş, C.: Automatic spike detection in EEG by a two-stage procedure based on support vector machines. Computers in Biology and Medicine 34, 561–575 (2004)
Nicolaou, N., Georgiou, J.: Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines. Expert Systems with Applications 39, 202–209 (2012)
Chandaka, S., Chatterjee, A., Munshi, S.: Cross-correlation aided support vector machine classifier for classification of EEG signals. Expert Systems with Applications 36, 1329–1336 (2009)
Polat, K., Güneş, S.: Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Applied Mathematics and Computation 187, 1017–1026 (2007)
Valenti, P., Cazamajou, E., Scarpettini, M., Aizemberg, A., Silva, W., Kochen, S.: Automatic detection of interictal spikes using data mining models. Journal of Neuroscience Methods 150, 105–110 (2006)
Polat, K., Güneş, S.: Artificial immune recognition system with fuzzy resource allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated identification system for classification of EEG signals. Expert Systems with Applications 34, 2039–2048 (2008)
Hur, J.: Multi-robot system control using Artificial Immune System. PhD Thesis, The University of Texas at Austin. Texas, United States (2007)
Timmis, J., Hone, A., Stibor, T., Clark, E.: Theoretical advances in artificial immune systems. Theoretical Computer Science 403, 11–32 (2008)
Timmis, J., Neal, M., Hunt, J.: An artificial immune system for data analysis. Biosystems 55, 143–150 (2000)
Engelbrecht, A.P.: Computational intelligence: an introduction. John Wiley & Sons, England (2007)
de Castro, L.N., Timmis, J.: Artificial immune systems: a new computational intelligence approach. Springer, London (2002)
Smith, S.L., Timmis, J.: An immune network inspired evolutionary algorithm for the diagnosis of Parkinson’s disease. Biosystems 94, 34–46 (2008)
Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: 1994 IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, pp. 202–212 (1994)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Perth, pp. 1942–1948 (1995)
Ba-Karait, N.O.S., Shamsuddin, S.M.: Handwritten Digits Recognition using Particle Swarm Optimization. In: Second Asia International Conference on Modeling & Simulation, pp. 615–619. IEEE Xplore, Kuala Lumpur (2008)
Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, pp. 81–86 (2001)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of 1998 IEEE International Conference on Evolutionary Computation: IEEE World Congress on Computational Intelligence, Anchorage, pp. 69–73 (1998)
Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review EÂ 64, 061907 (2001)
Wang, D., Miao, D., Xie, C.: Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Systems with Applications 38, 14314–14320 (2011)
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Ba-Karait, N.O., Shamsuddin, S.M., Sudirman, R. (2012). EEG Signals Classification Using a Hybrid Method Based on Negative Selection and Particle Swarm Optimization. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_34
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DOI: https://doi.org/10.1007/978-3-642-31537-4_34
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