(3.235.45.196)
Users online: 1358    [ij] [ij] [ij] 
Email id
 

Research Journal of Pharmacy and Technology
Year : 2015, Volume : 8, Issue : 12
First page : ( 1619) Last page : ( 1624)
Print ISSN : 0974-3618. Online ISSN : 0974-360X.
Article DOI : 10.5958/0974-360X.2015.00290.5

Automated detection of Epilepsy using Wavelet Features

Teja Padakandla Sai*, Narsimhan K.

School of Electrical and Electronics, SASTRA University, Thanjavur-613401, India

*Corresponding Author E-mail: saitejapadakandla2011@gmail.com

Online published on 1 January, 2016.

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.

Top

Keywords

Wavelet, Electroencephalogram, ictal, Epilepsy, Haar, sensitivity.

Top

  
║ Site map ║ Privacy Policy ║ Copyright ║ Terms & Conditions ║ Page Rank Tool
435,074,654 visitor(s) since 30th May, 2005.
All rights reserved. Site designed and maintained by DIVA ENTERPRISES PVT. LTD..
Note: Please use Internet Explorer (6.0 or above). Some functionalities may not work in other browsers.