Detection Of Epileptic Seizure From EEG Signal Using Wavelet Transform And Artificial Neural Network
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 10
Abstract
Epilepsy is the fourth most common neurological problem in which brain activities becomes abnormal, causing seizure or unusual behavior and sometimes loss of awareness. An electroencephalogram (EEG) signal is used to detect problems in electrical activity of the brain that is associated with certain brain disorders. In this paper, electroencephalogram signals were decomposed into the frequency sub-bands using DWT and set of statistical features were extracted from the sub-bands to represent the distribution of wavelet coefficients. Later, extracted features are given as an input to the neural network for classification of normal and epileptic signal. EEG signal is collected from the publically available Bonn dataset. Experimental results show that 99.8% accuracy is achieved in the classification using the proposed method.
Authors and Affiliations
M. Sornam, E. Panneer Selvam
Keywords
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- EP ID EP401961
- DOI 10.9790/9622-0810010611.
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