Abstract
The Electroencephalogram (EEG) is a complex signal that indicates the electrical activity of brain. EEG is a signal that represents that effect of the superimposition of diverse processes in the brain. Epilepsy is a common brain disorder. Out of hundred one person is suffering from this problem. Here we study a novel scheme for detecting epileptic seizure and classifying the risk level from EEG data recorded from Epileptic patients. EEG is obtained by International 10–20 electrodes system. Singular Value Decomposition (SVD) is used for feature extraction. The efficacy of the above methods is compared based on the bench mark parameters such as Performance Index (PI), and Quality Value (QV). A group of twenty patients with known epilepsy findings are analyzed. It was identified that Elman neural network is a good post classifier in the optimization of epilepsy risk levels.
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References
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Acknowledgments
The authors express their sincere thanks to the Management and the Principal of Bannari Amman Institute of Technology, Sathyamangalam for providing the necessary facilities for the completion of this paper. This research is also funded by AICTE RPS.:F No 8023/BOR/RID/RPS-41/2009-10, dated 10th Dec 2010.
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Harikumar, R., Ganeshbabu, C., Balasubramani, M., Sinthiya, P. (2013). Analysis of SVD Neural Networks for Classification of Epilepsy Risk Level from EEG Signals. In: S, M., Kumar, S. (eds) Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012). Lecture Notes in Electrical Engineering, vol 222. Springer, India. https://doi.org/10.1007/978-81-322-1000-9_3
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DOI: https://doi.org/10.1007/978-81-322-1000-9_3
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