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Analysis of Epileptic Seizures in Wavelet Domain

Published on April 2015 by K Najmah, M Bedeeuzzaman, Thasneem Fathima, P K Saleema
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National Conference on Advances in Computing Communication and Application
Foundation of Computer Science USA
ACCA2015 - Number 1
April 2015
Authors: K Najmah, M Bedeeuzzaman, Thasneem Fathima, P K Saleema
ba947172-0404-49bc-baa2-15cf771501cb

K Najmah, M Bedeeuzzaman, Thasneem Fathima, P K Saleema . Analysis of Epileptic Seizures in Wavelet Domain. National Conference on Advances in Computing Communication and Application. ACCA2015, 1 (April 2015), 13-15.

@article{
author = { K Najmah, M Bedeeuzzaman, Thasneem Fathima, P K Saleema },
title = { Analysis of Epileptic Seizures in Wavelet Domain },
journal = { National Conference on Advances in Computing Communication and Application },
issue_date = { April 2015 },
volume = { ACCA2015 },
number = { 1 },
month = { April },
year = { 2015 },
issn = 0975-8887,
pages = { 13-15 },
numpages = 3,
url = { /proceedings/acca2015/number1/20099-9003/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Computing Communication and Application
%A K Najmah
%A M Bedeeuzzaman
%A Thasneem Fathima
%A P K Saleema
%T Analysis of Epileptic Seizures in Wavelet Domain
%J National Conference on Advances in Computing Communication and Application
%@ 0975-8887
%V ACCA2015
%N 1
%P 13-15
%D 2015
%I International Journal of Computer Applications
Abstract

Epilepsy is a neurological disorder that can be assessed by electroencephalogram (EEG). EEG signals, which are highly non-linear and non-stationary in nature, are very difficult to characterize and interpret. Wavelet transform is a very effective tool for analyzing non-stationary signals. A method of automatic detection of epileptic seizures from scalp EEG is discussed in this paper. EEG signals are undergone wavelet decomposition and features such as mean and variance are extracted. A linear classifier is used for classification and could achieve an accuracy of 97. 19%.

References
  1. Gotman,J. 1982. Automatic Recognition of Epileptic Seizures in the EEG. Electroencephal. Clin. Neurophys. 54, 530-540.
  2. Gotman,J. 1990. Automatic Seizure Detection: Improvements and Evaluation. Electroencephal. Clin. Neurophys. 76, 317-324.
  3. Y. Liu, W. Zhou, Q. Yuan, S. Chen. 2012. Automatic Seizure Detection Using Wavelet Transform and SVM in Long-Term Intracranial EEG, IEEE Trans Neural Syst Rehabil Eng. 20, 749-755.
  4. Fathima,T. , Bedeeuzzaman,M. , Joseph,P. K. 2013 Wavelet based features for classification of normal, ictal and interictal EEG signals. J. Med. Health Info. 301-305
  5. Fathima,T. , Bedeeuzzaman,M. , Farooq,O. , Khan Y. U. 2011. Wavelet Based features for epileptic seizure detection, MES J. Technol. Manag. 2, 108-112.
  6. Wang, D. , Miao, D. , Xie, C. 2011. Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection, Expert Syst. Appl. 38, 14314-14320.
  7. Shafiul Alam S. M. , Bhuiyan M I H. 2013. Detection of Seizure and Epilepsy Using Higher Order Statistics in the EMD Domain. IEEE J. Biomed. Health Inform. 17, 312-318.
  8. Ahammad, N. , Fathima, T. , Joseph,P. Detection of Epileptic Seizure Event and Onset Using EEG. Hindawi Publishing Corporation . BioMed Research International Volume 2014, Article ID 450573, 7 pages.
Index Terms

Computer Science
Information Sciences

Keywords

Epileptic Seizure Eeg Wavelet Transform Linear Classifier