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Reseach Article

Epilepsy Prediction using Entropies

Published on February 2013 by Ashwini Holla V R, Akshatha Kamath, Sandeep Prabhu
International Conference on Electronic Design and Signal Processing
Foundation of Computer Science USA
ICEDSP - Number 4
February 2013
Authors: Ashwini Holla V R, Akshatha Kamath, Sandeep Prabhu
c1418f8b-5c1b-4b8f-9cfa-098a556af212

Ashwini Holla V R, Akshatha Kamath, Sandeep Prabhu . Epilepsy Prediction using Entropies. International Conference on Electronic Design and Signal Processing. ICEDSP, 4 (February 2013), 33-37.

@article{
author = { Ashwini Holla V R, Akshatha Kamath, Sandeep Prabhu },
title = { Epilepsy Prediction using Entropies },
journal = { International Conference on Electronic Design and Signal Processing },
issue_date = { February 2013 },
volume = { ICEDSP },
number = { 4 },
month = { February },
year = { 2013 },
issn = 0975-8887,
pages = { 33-37 },
numpages = 5,
url = { /specialissues/icedsp/number4/10375-1036/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 International Conference on Electronic Design and Signal Processing
%A Ashwini Holla V R
%A Akshatha Kamath
%A Sandeep Prabhu
%T Epilepsy Prediction using Entropies
%J International Conference on Electronic Design and Signal Processing
%@ 0975-8887
%V ICEDSP
%N 4
%P 33-37
%D 2013
%I International Journal of Computer Applications
Abstract

A person suffering from Epilepsy experiences or exhibits spontaneous seizures during which his behavior and perceptions are altered. Prediction of seizure onsets would help the affected and the bystanders to take prudent measures. Nonlinear features of Electro EncephaloGram (EEG) are used to isolate a class of background epileptic EEG, by training Support Vector Machine (SVM) classi?er. Very good accuracy results have been seen in the results.

References
  1. Faust,O. , U. Rajendra Acharya, Lim C. M, Bernhard H. Sputh, 2010,"Automatic Identi?cation of Epileptic and Background EEG Signals Using Frequency Domain Parameters", vol. 20.
  2. Faust, O. , U. Rajendra Acharya, Alen, A. , Lim, C. M. , 2008,"Analysisof EEG signals during epileptic and alcoholic states using AR modeling techniques", Innovations and Technology in Biology and Medicine,(ITBM-RBM). 29(1):4452, 2008.
  3. D. Puthankattil Subha, Paul K. Joseph, U. Rajendra Acharya, Lim,C. M. , 2010,"EEG Signal Analysis: A Survey", J. Medical Systems, vol. 34,no. 2, pg. no. 195-212, (2010).
  4. N. Kannathal, Lim C. M. , U. Rajendra Acharya, P. K. Sadasivan, 2005,"Entropies for detection of epilepsy in EEG",J. Computer Methods and Programs in Biomedicine, December 2005, Vol. 80, Issue 3, pp. 187-194.
  5. St´ephane Mallat, 1999, "A Wavelet Tour of Signal Processing", San Diego:Academic Press,
  6. Tarassenko L, Khan Y. U, Holt M. R. G,1998, "Identi?cation of inter-ictal spikes in the EEG using neural network analysis," Inst. Elect. Eng. Proc. Sci. Meas. Technol, Nov. 1998 vol. 145, no. 6, pp. 270278.
  7. Indiradevi K. P, Elizabeth Elias, Sathidevi P. S, Nayak S. Dinesh, Radhakrishnan K. ,2008, "A multi-levelwavelet approach for automatic detection of epileptic spikes in the electroencephalogram" Computers in Biology and Medicine Vol. 38, 7, 2008, pp. 805 - 816 .
  8. Subasi A, 2007, "Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction", Computers in Biology and Medicine, 2007, 37(2), 227244 .
  9. Kannathal N, U. Rajendra Acharya, Lim C. M, Sadasivan P. K, 2005, "Characterization of EEG - A comparative study ", J. Computer Methods and Programs in Biomedicine, Oct 2005 , Vol. 80, Issue 1, pp 17-23. Andrzejak R. G, 2001, "Indications of nonlinear deterministic and ?nite dimensional structures in time series of brain electrical activity: Dependence on region and brain state", Physical Review E, 2001 vol. 64(6).
  10. Franc, Vojt?ech and Hlav´a?c, V´aclav, 2004,Statistical Pattern Recognition Toolbox for MatlabR, Research Report, pub: Center for Machine Perception, Czech Technical University, available at ftp://cmp. felk. cvut. cz/pub/cmp/articles/franc/Franc-TR-2004-08. pdf .
  11. Kannathal N, Lim C. M, U. Rajendra Acharya, Sadasivan P. K, 2005,"Entropies for detection of epilepsy in EEG", J. Computer Methods and Programs in Biomedicine, Dec. 2005, Vol. 80, Issue 3, pp. 187-194.
  12. Hasan Ocak, 2009, "Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy", J Expert Systems with Applications 36, 20272036, Elsiever Publications.
  13. The data from Bonn University is available at http: www. meb. uni-bonn. de/epileptologie/science/physik/eegdata. html.
  14. Pincus, S. M. , 2000,"Approximate entropy as a measure of system complexity",Proc. Natl. Acad. Sci. USA. , vol. 88, pp. 2297-2301.
  15. Costa M, Goldberger A. L, Peng C. K, 2005,"Multiscale entropy analysis ofbiological signals", Phys Rev E 2005,71:021906.
  16. Richman, Joshua S, Moorman, J. Randall, 2000,"Physiological time-series analysis using approximate entropy and sample entropy", Am J Physiol Heart Circ Physiol, 2000, vol. 278, pp. H2039–2049.
  17. David R. Brillinger, 1994,"Some basic aspects and uses of higher-order spectra", Signal Processing, vol. 36, 3, pp. 239-249.
  18. U. Rajendra Acharya, Eric Chern-P. C, Chua K. C, Lim C. M, Toshiyo Tamura, 2010, "Analysis and Automatic Identi?cation of Sleep Stages Using Higher Order Spectra", Int. J. Neural Syst, 2010, vol. 20, no. 6, pp 509-521.
  19. Srinivasan V, Eswaran C, Sriram N, 2007, "Approximate Entropy-Based Epileptic EEG Detection Using Arti?cial Neural Networks",IEEE Transactions on Information Technology In Biomedicine, May 2007, Vol. 11, 3, pp. 288-295.
  20. Misra,U. K, Kalitha J, 2005,Clinical Electroencephalography, Elsevier India.
  21. Hastie T, Tibshirani R, Friedman J. H, 2009, The elements of statistical learning : data mining, inference, and prediction, Springer.
  22. Ashwini V R, Aparna Dinesh, 2012, " A Nearest Neighbor Based Approach for Classifying Epileptiform EEG Using NonLinear DWT Features", IEEE conference on Signal Processing and Communication SPCOM 2012, IISc.
Index Terms

Computer Science
Information Sciences

Keywords

Electro Encephalogram (eeg) Support Vector Machine (svm) Wavelets Non Linear Features