CFP last date
20 December 2024
Reseach Article

Automatic Seizure Detection using Inter Quartile Range

by M. Bedeeuzzaman, Omar Farooq, Yusuf U Khan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 11
Year of Publication: 2012
Authors: M. Bedeeuzzaman, Omar Farooq, Yusuf U Khan
10.5120/6304-8614

M. Bedeeuzzaman, Omar Farooq, Yusuf U Khan . Automatic Seizure Detection using Inter Quartile Range. International Journal of Computer Applications. 44, 11 ( April 2012), 1-5. DOI=10.5120/6304-8614

@article{ 10.5120/6304-8614,
author = { M. Bedeeuzzaman, Omar Farooq, Yusuf U Khan },
title = { Automatic Seizure Detection using Inter Quartile Range },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 11 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number11/6304-8614/ },
doi = { 10.5120/6304-8614 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:35:13.964231+05:30
%A M. Bedeeuzzaman
%A Omar Farooq
%A Yusuf U Khan
%T Automatic Seizure Detection using Inter Quartile Range
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 11
%P 1-5
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The statistical properties of seizure EEG are found to be different from that of the normal EEG. This paper ascertains the efficacy of inter quartile range (IQR), a median based measure of statistical dispersion, as a discriminating feature that can be used for the classification of EEG signals into normal, interictal and ictal classes. IQR along with variance and entropy are calculated for each frame of EEG. To reduce the feature vector size, standard statistical features such as mean, minimum, maximum and standard deviation were evaluated and were given as input to a linear classifier. Without resorting to any kind of transformation, the proposed method reduces the computational complexity and achieves a classification accuracy of 100%.

References
  1. Ihle, M. , Feldwisch-Drentrup, H. , Teixeira, C. A. ,Witon, A. , Schelter, B. , Timmer, J. , Schulze-Bonhage, A. 2010 EPILEPSIAE- A European epilepsy database, Comput. Methods Programs Biomed. doi:10. 1016/j. cmpb. 2010. 08. 011
  2. Guler, N. F. , Ubeyli, E. D. , and Guler, I. 2005 Recurrent neural networks employing Lyapunov exponents for EEG signal classification, Expert Syst. Appl. 29, 507-514.
  3. Polat, K. and Gu¨nes, S. 2008 Artificial immune recognition system with fuzzy resource allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated identification system for classification of EEG signals, Expert Syst. Appl. 34, 2039-2048.
  4. Guo, L. , Rivero, D. and Pazos, A. 2010 Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks, J. Neurosci. Methods 193 (1), 156-163.
  5. Tito, M. , Cabrerizo, M. , Ayala, M. , Barreto, A. , Miller, I. , Jayakar, P. , and Adjouadi, M. 2009 Classification of electroencephalographic seizure recordings into ictal and interictal files using correlation sum, Comput. Biol. Med. 39, 604 – 614.
  6. Wang, D. , Miao, D. and Xie, C. 2011 Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classi?cation for epileptic detection, Expert Syst. Appl. 38, 14314–14320.
  7. Khan, Y. U. and Gotman, J. 2003 Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin. Neurophysiol. 114, 898-908.
  8. Hsu, K. and Yu, S. 2010 Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm. Comput. Biol. Med. 40, 823-830.
  9. Patnaik, L. M. and Manyam, O. K. 2008 Epileptic EEG detection using neural networks and post-classification. Comput. Methods Programs Biomed. 91, 100-109.
  10. Temko, A. , Thomas, E. , Marnane, W. , Lightbody, G. and Boylan, G. 2011 EEG-based neonatal seizure detection with Support Vector Machines, Clin. Neurophysiol. 122, 464–473.
  11. Naghsh-Nilchi, A. R. and Aghashahi, M. 2010 Epilepsy seizure detection using eigen-system spectral estimation and Multiple Layer Perceptron neural network, Biomed. Signal Process. Control 5, 147–157.
  12. Subasi, A. 2007 Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction, Comput. Biol. Med. 37, 227 – 244.
  13. Andrzejak, R. G. , Lehnertz, K. , Mormann, F. , Rieke, C. , David, P. and Elger, C. E. 2001 Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys. Rev. E 64 (061907), 1-8.
  14. Ghosh-dastidar, S. , Adeli, H. and Dadmehr, N. 2007 Mixed-bandwavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection, IEEE Trans. Biomed. Eng. 54, 1545-1551.
  15. Übeyli, E. D. 2009 Combined neural network model employing wavelet coefficients for EEG signals classification, Digital Signal Process. 19, 297-308.
  16. Song, Y. and Liò, P. 2010 A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine, J. Biomedical Science and Engineering 3 556-567. doi:10. 4236/jbise. 2010. 36078
  17. Tzallas, A. T. , Tsipouras, M. G. and Fotiadis, D. I. 2009 Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis. IEEE Trans. Inf. Technol. Biomed. 13, 703-710.
  18. Ocak, H. 2008 Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm, Signal Process. 88, 1858-1867.
  19. Aarabi, A. , Wallois, F. and Grebe, R. 2005 Feature selection based on discriminant and redundancy analysis applied to seizure detection in newborn. In Proceedings of 2nd International IEEE EMBS Conference on Neural Engineering, Arlington, Virginia, 241-244.
  20. Yildiz, A. , Akin, M. , Poyraz, M. and Kirbas, G. 2009 Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction. Expert Syst. Appl. 36 (4), 7390-7399.
  21. Duda, R. O. , Hart, P. E. and Stork, D. G. 2003 Pattern Classification, 2nd ed. , Singapore: John-Wiley & Sons (Asia) Pte. Ltd.
Index Terms

Computer Science
Information Sciences

Keywords

Electroencephalogram Epilepsy Feature Extraction Inter Quartile Range Classification