Author(s): Padakandla Sai Teja, K. Narsimhan

Email(s): saitejapadakandla2011@gmail.com

DOI: 10.5958/0974-360X.2015.00290.5   

Address: Padakandla Sai Teja*, K. Narsimhan
School of Electrical and Electronics, SASTRA University, Thanjavur-613401, India.
*Corresponding Author

Published In:   Volume - 8,      Issue - 12,     Year - 2015


ABSTRACT:
Epilepsy is generally considered as a group of neurological disorders characterized by epileptic seizures. It is often confirmed with an electroencephalogram (EEG). But identification of epilepsy has to be done by skilled neurologist. This paper proposes an efficient methodology for automatic detection of ictal and healthy EEG signals which is the ultimate goal of machine learning, which has performed efficient classification .We used discrete wavelet transform for feature extraction and obtained wavelet coefficients .Neural network pattern recognition tool is used for classification .The performance of the proposed method is evaluated using in terms of sensitivity ,specifity and accuracy.


Cite this article:
Padakandla Sai Teja, K. Narsimhan. Automated detection of Epilepsy using Wavelet Features. Research J. Pharm. and Tech. 8(12): Dec., 2015; Page 1619-1624. doi: 10.5958/0974-360X.2015.00290.5

Cite(Electronic):
Padakandla Sai Teja, K. Narsimhan. Automated detection of Epilepsy using Wavelet Features. Research J. Pharm. and Tech. 8(12): Dec., 2015; Page 1619-1624. doi: 10.5958/0974-360X.2015.00290.5   Available on: https://www.rjptonline.org/AbstractView.aspx?PID=2015-8-12-4


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RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

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