DocumentCode
2532459
Title
Automated detection of epileptic seizures using wavelet entropy feature with recurrent neural network classifier
Author
Kumar, S. Pravin ; Sriraam, N. ; Benakop, P.G.
Author_Institution
Dept. of Biomed. Eng., SSN Coll. of Eng., Kalavakkam
fYear
2008
fDate
19-21 Nov. 2008
Firstpage
1
Lastpage
5
Abstract
Electroencephalograms (EEG) are the brain signals that provide us the valuable information about the normal or epileptic state of the brain. In this paper the EEG signals were characterized by wavelet, sample and spectral entropy approach and the recurrent neural network classifier is used for the automated detection of epileptic seizures.
Keywords
electroencephalography; entropy; medical signal detection; neural nets; wavelet transforms; EEG signals; automated detection; brain signals; electroencephalograms; epileptic seizures; epileptic state; recurrent neural network classifier; spectral entropy approach; wavelet entropy feature; Artificial neural networks; Biological neural networks; Biomedical engineering; Educational institutions; Electrodes; Electroencephalography; Entropy; Epilepsy; Recurrent neural networks; Wavelet coefficients; classification; eeg; epilepsy; recurrent neural network; wavelet entropy;
fLanguage
English
Publisher
ieee
Conference_Titel
TENCON 2008 - 2008 IEEE Region 10 Conference
Conference_Location
Hyderabad
Print_ISBN
978-1-4244-2408-5
Electronic_ISBN
978-1-4244-2409-2
Type
conf
DOI
10.1109/TENCON.2008.4766836
Filename
4766836
Link To Document