Cover Image

Development of EEG-based epileptic detection using artificial neural network

Link to publisher's homepage at http://ieeexplore.ieee.org/

Saved in:
Bibliographic Details
Main Authors: Azian Azamimi, Abdullah, Saufiah, Abdul Rahim, Adira, Ibrahim
Other Authors: azamimi@unimap.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2012
Subjects:
Epilepsy
Electroencephalogram (EEG)
Discrete Wavelet Transform (DWT)
Fast Fourier Transform (FFT)
Artificial neural network
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/21437
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimap-21437
record_format dspace
spelling my.unimap-214372012-10-18T08:55:58Z Development of EEG-based epileptic detection using artificial neural network Azian Azamimi, Abdullah Saufiah, Abdul Rahim Adira, Ibrahim azamimi@unimap.edu.my saufiah@unimap.edu.my adira.ibrahim@yahoo.com Epilepsy Electroencephalogram (EEG) Discrete Wavelet Transform (DWT) Fast Fourier Transform (FFT) Artificial neural network Link to publisher's homepage at http://ieeexplore.ieee.org/ Epilepsy is one of the most common neurological disorders causing from repeating brain seizures that are the result of the temporal and sudden electrical disturbance of the brain. Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. This project proposed to develop a system that can detect epilepsy based on EEG signal using artificial neural network. Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) were applied as feature extraction methods. These features then set as input to the feedforward neural network with backpropagation training algorithm to get the classification accuracy. The accuracy of DWT with 10000 epochs is 97% while accuracy of FFT method gives 53.889% accuracy. The combination of DWT and FFT extracted features give the highest accuracy, which is 98.889%. The classification accuracy depends on the number of epoch and the features from the feature extraction. Increased number of epoch gives long response time to train the network. 2012-10-18T08:55:58Z 2012-10-18T08:55:58Z 2012-02-27 Working Paper p. 605-610 978-145771989-9 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6178989 http://hdl.handle.net/123456789/21437 en Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012) Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Epilepsy
Electroencephalogram (EEG)
Discrete Wavelet Transform (DWT)
Fast Fourier Transform (FFT)
Artificial neural network
spellingShingle Epilepsy
Electroencephalogram (EEG)
Discrete Wavelet Transform (DWT)
Fast Fourier Transform (FFT)
Artificial neural network
Azian Azamimi, Abdullah
Saufiah, Abdul Rahim
Adira, Ibrahim
Development of EEG-based epileptic detection using artificial neural network
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 azamimi@unimap.edu.my
author_facet azamimi@unimap.edu.my
Azian Azamimi, Abdullah
Saufiah, Abdul Rahim
Adira, Ibrahim
format Working Paper
author Azian Azamimi, Abdullah
Saufiah, Abdul Rahim
Adira, Ibrahim
author_sort Azian Azamimi, Abdullah
title Development of EEG-based epileptic detection using artificial neural network
title_short Development of EEG-based epileptic detection using artificial neural network
title_full Development of EEG-based epileptic detection using artificial neural network
title_fullStr Development of EEG-based epileptic detection using artificial neural network
title_full_unstemmed Development of EEG-based epileptic detection using artificial neural network
title_sort development of eeg-based epileptic detection using artificial neural network
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/21437
_version_ 1643793397975089152
score 13.214268

Similar Items