Epilepsy is a common brain disorder that affects one out of hundred patients. EEG (electroencephalogram) is a signal that represents that effect of the superimposition of diverse processes in the brain. This paper investigates the possibility of Extreme Learning Machine (ELM) as a classifier for detecting and classifies the epilepsy of various risk levels from the EEG signals. The Singular Value Decomposition (SVD) is used for dimensionality reduction. Twenty patients are analysed in this study.

1. R. Harikumar, C. Ganesh Babu, P. Sinthiya, M. Balasubramani,"Performance analysis of SVD neural networks for classification of epilepsy risk level from EEG signal", Lecture notes in Electrical Engineering 222,DOI:10. 1007/978-81-322-1000-9_3,pp 27-34,Springer 2013.
2. F. Jiang, R. Kannan, M. Littman, S. Vempala, Efficient Singular Value Decomposition Via Improved Document Sampling. Technical Report. CS-99-5, Department of Computer Science, Duke University, 1999.
3. Abdi H, Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition(GSVD). Encyclopedia of Measurement and Statistics. Thousand Oaks: Sage Publications; 2007,907–912.
4. Stewart GW, "On the early history of the singular value decomposition", SIAM Rev1993, 35:551–566.
5. G. B. Huang ,Q. Y. Zhu and C. K. Siew, "Extreme learning machine : A new learning scheme of feedforward neural networks" in proceedings of international joint conference on neural networks. (IJCNN 20004)(budapest, Hungary),25-29 July, 2004.
6. G. B. Huang and C. K. Siew, "Extreme learning machine: RBF network case", in proceedings of eighth international conference on control automation, robotics and vision(ICARCV 2004), (Kunming, China) , 6-9 December 2004.
7. G. B. Huang and C. K. Siew, "Extreme learning machine with randomly assigned RBK kernels", international journal of information technology, Vol. 11, no. 1,2005.
8. Wang D. and Huang, G. B. (2005), "Protein sequence classification using extreme learning machine". Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, Montreal, 3, 31 July-4 August 2005, 53-59.
9. R. Zhang, R. , Huang, G. B. , Sundararajan, N. and Saratchandran, P. , " Multicategory classifcation using an extreme learning machine for microarray gene expression cancer diagnosis" IEEE Transactions on Computational Biology and Bioinformatics, 2007 . 4(3), 485-495.
10. Liang, N. Y. , Saratchandran, P. , Huang, G. B. and Sundararajan, N. "Classification of mental tasks from EEG signals using extreme learning machine". International Journal of Neural Systems 2006, 16(1), 29-38.
11. Kim, J. , Shin, H. , Lee, Y. and Lee, M. "Algorithms for classifying arrhythmia using extreme learning machine and principle component analysis", Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, 22-26 August 2007, 3257-3260.
12. Y. Song ,P. Lio, "A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine", J. Biomedical Science and Engineering , 2010, 3, 556-567.
13. G. Geetha, S . N. Geethalakshmi, "Detecting epileptic seizure using electroencephalogram: A new and optimized method for seizure classification using hybrid extreme learning machine". International conference on Process automation, control and computing (PACC), July 2011. Page no 1-6.
14. Guang-Bin Huang and Haroon A. Babri, "Upper Bounds on the Number of Hidden Neurons in Feedforward Networks with Arbitrary Bounded Nonlinear Activation Functions", IEEE Transactions On Neural Networks, Vol. 9, No. 1, January 1998.
15. Guang-Bin Huang, Qin-Yu Zhu, K. Z. Mao, K. Z. Mao, P. Saratchandran, N. Sundararajan, "Can Threshold Networks be Trained Directly?", IEEE Transactions On Circuits And Systems—II: Express Briefs, Vol. 53, No. 3, March 2006.

Extreme Learning Machine Singular Value Decomposition Epilepsy Risk Level Seizure.