Wavelet based features for classification of normal, ictal and interictal EEG signals

Journal of Medical Imaging and Health Informatics

Volumn 3, Issue 2, 2013, Pages 301-305

  Fathima, Thasneem ^a   Bedeeuzzaman, M ^b   Joseph, Paul K ^a  

^a NATIONAL INSTITUTE OF TECHNOLOGY CALICUT   (India)

^b MES COLLEGE OF ENGINEERING   (India)

Author keywords

Electroencephalogram; Inter Quartile Range; Seizure Detection; Wavelet Transform

Indexed keywords

ARTICLE; ELECTRODE; ELECTROENCEPHALOGRAM; EPILEPSY; EPILEPTOGENESIS; HEMISPHERE; HUMAN; SEIZURE; SENSITIVITY AND SPECIFICITY; SLEEP WAKING CYCLE; WAVELET ANALYSIS;

EID: 84881238719     PISSN: 21567018     EISSN: 21567026     Source Type: Journal    
DOI: 10.1166/jmihi.2013.1161     Document Type: Article

References (41)

1. C. G. Mosquera, A. M. Trigueros, and A. N. Vazquez, Epilepsy- Histological, Electroencephalographic and Psychological Aspects, edited by D. Stevanovic, In Tech, Rijeka, Croatia (2012), pp. 49-74.
2. D. P. Subha, K. P. Joseph, U. R. Acharya, and C. M. Lim, EEG signal processing: A survey. J. Med. Sys. 34, 195 (2010).
3. C. K. Chua, V. Chandran, U. R. Acharya, and C. M. Lim, Application of higher order statistics/spectra in biomedical signals-A review. Medical Engineering and Physics 32, 679 (2010).
4. K. C. Chua, V. Chandran, U. R. Acharya, and C. M. Lim, Cardiac state diagnosis using higher order spectra of heart rate variability. J. Med. Eng. Technol. 32, 145 (2006).
5. U. R. Acharya, O. Faust, N. Kannathal, T. J. Chua, and S. Laxminarayan, Dinamical analysis of EEG signals at various sleep stages. Comput. Methods Programs Biomed. 80, 37 (2005).
6. R. S. Fisher, W. V. E. Boas, W. Blume, C. Elger, P. Genton, P. Lee, and J. Engel, Epileptic seizures and epilepsy: Definitions proposed by the international league against epilepsy (ILAE) and the international bureau for epilepsy (IBE). Epilepsia 46, 470 (2005).
7. A. S. Zandi, M. Javidan, G. A. Dumont, and R. Tafreshi, Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEE Trans. Biomed. Eng. 57, 1639 (2010).
8. P. F. Prior, R. S. M. Virden, and D. E. Maynard, An EEG device for monitoring seizure discharges. Epilepsia 14, 367 (1973).
9. J. R. Ives, C. J. Thompson, P. Gloor, A. Olivier, and J. F. Woods, The online computer detection and recording of spontaneous temporal lobe epileptic seizures from patients with implanted depth electrodes via a radio-telemetry link. Electroencephalogr. Clin. Neurophysiol. 37, 205 (1974).
10. J. Gotman, Automatic recognition of epileptic seizures in the EEG, electroencephalogr. Clin. Neurophysiol. 54, 530 (1982).
11. G. W. Harding, An automated seizure monitoring system for patients with indwelling recording electrodes. Electroencephalogr. Clin. Neurophysiol. 86, 428 (1993).
12. H. Qu and J. Gotman, A patient specific algorithm for the detection of seizure onset in long-term EEG monitoring; possible use as a warning device. IEEE Trans. Biomed. Eng. 44, 115 (1997).
13. K. Polat and S. Gunes, 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 (2008).
14. E. D. Ubeyli, Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines. Comput. Biol. Med. 38, 14 (2008).
15. A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans. Inf. Technol. Biomed. 13, 703 (2009).
16. N. Nicolaou and J. Georgiou, Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst. Appl. 39, 202 (2012).
17. Y. Tang and D. M. Durand, A tunable support vector machine assembly classifier for epileptic seizure detection. Expert Syst. Appl. 39, 3925 (2012).
18. N. Kannathal, C. M. Lim, U. R. Acharya, and P. K. Sadasivan, Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed. 80, 187 (2005).
19. U. R. Acharya, E. C. P. Chua, C. K. Chua, C. M. Lim, and T. Tamura, Analysis and automatic identification of sleep stages using higher order spectra. International Journal of Neural Systems 20, 509 (2010).
20. C. K. Chua, V. Chandran, U. R. Acharya, and C. M. Lim, Analysis of epileptic EEG signals using higher order spectra. J. Med. Eng. Technol. 33, 42 (2009).
21. C. K. Chua, V. Chandran, U. R. Acharya, and C. M. Lim, Application of higher order spectra to identify epileptic EEG. J. Med. Sys. 35, 1563 (2011).
22. U. R. Acharya, C. K. Chua, T. C. Lim, Dorithy, and J. S. Suri, Automatic identification of epileptic EEG signals using nonlinear parameters. J. Mech. Med. Biol. 9, 539 (2009).
23. C. K. Chua, V. Chandran, U. R. Acharya, and C. M. Lim, Automatic identification of epileptic EEG signals using higher order spectra. J. Eng. Med. 223, 485 (2009).
24. O. Faust, U.R. Acharya, C. M. Lim, and B. Sputh, Automatic identification of epileptic and background EEG signals using frequency domain parameters. Int. J. Neural Syst. 20, 159 (2010).
25. U. R. Acharya, S. Vinitha, and J. S. Suri, Use of principal component analysis for automatic detection of epileptic EEG activities. Expert Syst. Appl. 39, 9072 (2012).
26. T. Fathima, M. Bedeeuzzaman, O. Farooq, and Y. U. Khan, Wavelet based features for epileptic seizure detection. MES J. Technol. Managel. 2, 108 (2011).
27. U. R. Acharya, S. Vinitha, and J. S. Suri, Automatic detection of epileptic EEG signals using higher order cumulant features. Int. J. Neural Syst. 21, 1 (2011).
28. U. R. Acharya, F. Molinari, S. Vinitha Sree, S. Chattopadhyay, K. H. Ng, and J. S. Suri, Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Proces. 7, 401 (2012).
29. M. Bedeeuzzaman, O. Farooq, and Y. U. Khan, Dispersion Measures and Entropy for Seizure Detection, IEEE-ICASSP-2011, Prague, May (2011), pp. 159-163.
30. U. R. Acharya, S. Vinitha, S. Chattopadhyay, W. Yu, and A. P. C. Alvin, Application of recurrence quantification analysis for the automated identification of epileptic EEG signals. Int. J. Neural Syst. 21, 199 (2011).
31. N. Hazarika, J. Z. Chen, A. C. Tsoi, and A. Sergejew, Classification of EEG signals using the wavelet transform. Signal Processing 59, 61 (1997).
32. Y. U. Khan and J. Gotman, Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin. Neurophysiol. 114, 898 (2003).
33. M. K. Kiymik, I. Guler, A. Dizibuyuk, and M. Akin, Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Comput. Biol. Med. 35, 603 (2005).
34. A. Subasi, EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst. Appl. 32, 1084 (2007).
35. H. Ocak, Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 36, 2027 (2009).
36. U. R. Acharya, R. Yanti, G. Swapna, V. S. Sree, R. J. Martis, and J. S. Suri, Automated diagnosis of epileptic electroencephalogram using independent component analysis and discrete wavelet transform for different electroencephalogram durations, Proceedings of the Institution of Mechanical Engineers, Part H. J. Eng. Med. (2012), (Doi: 10.1177/0954411912467883)
37. C. D. Binnie, R. Cooper, F. Mauguiere, J. Osselton, P. F. Prior, and B. M. Tedman, Clinical Neurophysiology EEG, Paediatric Neurophysiology, Special Techniques and Applications, Elsievier Academic Press, Amsterdam (2003), Vol. 2, pp. 90-92.
38. Y. U. Khan and O. Farooq, Autoregressive features based classification for seizure detection using neural network in scalp electroencephalogram. Int. J. Biomed. Eng. Technol. 2, 370 (2009).
39. http://www.meb.unibonn.de/epileptologie/science/physik/eegdata; Accessed June (2012).
40. R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, 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, 1 (2010).
41. Vincent J. Samar, Wavelet analysis of neuroelectric waveforms: A conceptual tutorial. Brain Lang. 66, 7 (1999).