Research Article
Automated Event Detection of Epileptic Spikes using Neural Networks
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 2 - Issue 4 |
| Published: June 2010 |
| Authors: Payal Khanwani, Susmita Sridhar, Mrs.K.Vijaylakshmi |
10.5120/660-928
|
Payal Khanwani, Susmita Sridhar, Mrs.K.Vijaylakshmi . Automated Event Detection of Epileptic Spikes using Neural Networks. International Journal of Computer Applications. 2, 4 (June 2010), 14-17. DOI=10.5120/660-928
@article{ 10.5120/660-928,
author = { Payal Khanwani,Susmita Sridhar,Mrs.K.Vijaylakshmi },
title = { Automated Event Detection of Epileptic Spikes using Neural Networks },
journal = { International Journal of Computer Applications },
year = { 2010 },
volume = { 2 },
number = { 4 },
pages = { 14-17 },
doi = { 10.5120/660-928 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2010
%A Payal Khanwani
%A Susmita Sridhar
%A Mrs.K.Vijaylakshmi
%T Automated Event Detection of Epileptic Spikes using Neural Networks%T
%J International Journal of Computer Applications
%V 2
%N 4
%P 14-17
%R 10.5120/660-928
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Biomedical signals carry signatures of physiological events. The part of the signal related to specific event is called epoch. Epilepsy is one of the important brain disorders which can be diagnosed and monitored is characterized by sudden recurrent and transient disturbances of mental function and movements of body which is caused from excessive discharge of brain cell groups. This excessive discharge is shown in EEG as epileptic spikes which are complementary source of information in diagnosis and localization of epilepsy. Artificial Neural networks have been provided an effective approach for a broad spectrum of applications for EEG signals because of its self-adaption and natural way to organize and implement the redundancy. It is well known that back-propagation networks are very suitable for pattern recognitions. The algorithm tested on 100 normal and abnormal datasets showed expected classification.
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