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
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;
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
DOI :
10.1109/TENCON.2008.4766836
