© The Institution of Engineering and Technology
Electroencephalogram (EEG) has a great potential for diagnosis and treatment of brain disorders like epileptic seizure. Feature extraction and classification of EEG signals is the crucial task to detect the stages of ictal and interictal signals for treatment and precaution of epileptic patients. However, existing seizure and non-seizure feature extraction techniques are not good enough for the classification of ictal and interictal EEG signals considering the non-abruptness phenomena and inconsistency in different brain locations. In this study, the authors present a new approach for feature extraction and classification by exploiting temporal correlation within EEG signals for better seizure detection as any abruptness in the temporal correlation within a signal represents the transition of a phenomenon. In the proposed methods, they divide an EEG signal into a number of epochs and arrange them into two-dimensional matrix and then apply different transformation/decomposition to extract a number of statistical features. These features are then used as an input into LS-SVM to classify them. Experimental results show that the proposed methods outperform the existing state-of-the-art method for better classification in terms of sensitivity, specificity and accuracy of ictal and interictal period of epilepsy for benchmark datasets and different brain locations.
References
-
-
1)
-
23. Gupta, D., James, C.J., Gray, W.P.: ‘Phase synchronization with ICA for epileptic seizure onset prediction in the long term EEG’. IET Int. Conf. on Advances in Medical, Signal and Information Processing, 2008, pp. 1–4.
-
2)
-
28. Paul, M., Evans, C., Murshed, M.: ‘Disparity-adjusted 3D multi-view video coding with dynamic background modeling’. IEEE Int. Conf. on Image Processing, 2013, pp. 1719–1723.
-
3)
-
30. Wang, H., Hu, D.: ‘Comparison of SVM and LS-SVM for regression’. Int. Conf. on Neural Networks and Brain, 2005, pp. 279–283.
-
4)
-
20. Clercq, W.De, Vergult, A., Vanrumste, B., Paesschen, W.V., Huffel, S.V.: ‘Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram’, IEEE Trans. Biomed. Eng., 2006, 53, (12), pp. 2583–2587 (doi: 10.1109/TBME.2006.879459).
-
5)
-
18. Williamson, J.R., Bliss, D.W., Browne, D.W., Narayanan, J.T.: ‘Seizure prediction using EEG spatiotemporal correlation structure’, Epilepsy Behav., 2012, 25, (2), pp. 230–238 (doi: 10.1016/j.yebeh.2012.07.007).
-
6)
-
10. Polat, K., Güunes, S.: ‘Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast fourier transform’, Appl. Math. Comput., 2007, 187, (2), pp. 1017–1026 (doi: 10.1016/j.amc.2006.09.022).
-
7)
-
24. Hyvärinen, A., Oja, E.: ‘Independent component analysis: algorithms and applications’, Neural Netw., 2000, 13, (4–5), pp. 411–430 (doi: 10.1016/S0893-6080(00)00026-5).
-
8)
-
9)
-
4. Estrada, E., Nazeran, H., Ebrahimi, F., Mikaeili, M.: ‘EEG signal features for computer-aided sleep stage detection’. IEEE EMBS Conf. on Neural Engineering, 2009, pp. 669–672.
-
10)
-
13. Pachori, R.B., Bajaj, V.: ‘Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition’, Comput. Methods Programs Biomed., 2011, 104, (3), pp. 373–381 (doi: 10.1016/j.cmpb.2011.03.009).
-
11)
-
16. Bajaj, V., Pachori, R.B.: ‘Classification of seizure and non-seizure EEG signals using empirical mode decomposition’, IEEE Trans. Inf. Technol. Biomed., 2012, 16, (6), pp. 1135–1142 (doi: 10.1109/TITB.2011.2181403).
-
12)
-
5. Hanafiah, Z.M., Yunos, K.F.M., Murat, Z.H., Taib, M.N., Lias, S.: ‘EEG brainwave pattern for smoking behaviour after horizontal rotation treatment’. IEEE Student Conf. on Research and Development, 2009, pp. 559–561.
-
13)
-
17. Suykens, J.A.K., Vandewalle, J.: ‘Least squares support vector machine classifiers’, Neural Process. Lett., 1999, 9, (3), pp. 293–300 (doi: 10.1023/A:1018628609742).
-
14)
-
2. Scholler, S., Bosse, S., Treder, M.S., et al: ‘Toward a direct measure of video quality perception using EEG’, IEEE Trans. Image Process., 2012, 21, (5), pp. 2619–2629 (doi: 10.1109/TIP.2012.2187672).
-
15)
-
16)
-
17)
-
25. LeVan, P., Urrestarazu, E., Gotman, J.: ‘A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification’, Clin. Neurophysiol., 2006, 117, (4), pp. 912–927 (doi: 10.1016/j.clinph.2005.12.013).
-
18)
-
22. Mihandoost, S., Amirani, M., Mazlaghani, M., Mihandoost, A.: ‘Automatic feature extraction using generalised autoregressive conditional heteroscedasticity model: an application to electroencephalogram classification’, IET Signal Process., 2012, 6, (9), pp. 829–838 (doi: 10.1049/iet-spr.2011.0338).
-
19)
-
29. Xing, H., Ha, M., Hu, B., Tian, D.: ‘Linear feature- weighted support vector machine’ (Springer and Fuzzy Information and Engineering Branch of the Operations Research Society of China, 2009).
-
20)
-
1. Soleymani, M., Pun, T., Pantic, M.: ‘Multi-modal emotion recognition in response to videos’, IEEE Trans. Affective Comput., 2012, 3, (2), pp. 211–223 (doi: 10.1109/T-AFFC.2011.37).
-
21)
-
8. Dastidar, S.G., Adeli, H., Dadmehr, N.: ‘Mixed-band wavelet chaos-neural network methodology for epilepsy and epileptic seizure detection’, IEEE Trans. Biomed. Eng., 2007, 54, (9), pp. 1545–1551 (doi: 10.1109/TBME.2007.891945).
-
22)
-
3. Di, W., Zhihua, C., Ruifang, F., Guangyu, L., Tian, L.: ‘Study on human brain after consuming alcohol based on EEG signal’. Int. Conf. on Computer Science and Information Technology, 2010, pp. 406–409.
-
23)
-
12. Pachori, R.B.: ‘Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition’, Res. Lett. Signal Process., , 2008.
-
24)
-
14. Bajaj, V., Pachori, R.B.: ‘Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals’, Biomed. Eng. Lett., 2013, 3, (1), pp. 17–21 (doi: 10.1007/s13534-013-0084-0).
-
25)
-
9. Ocak, H.: ‘Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm’, Signal Process., 2008, 88, (7), pp. 1858–1867 (doi: 10.1016/j.sigpro.2008.01.026).
-
26)
-
32. Paul, M., Frater, M., Arnold, J.: ‘An efficient mode selection prior to the actual encoding for H.264/AVC encoder’, IEEE Trans. Multimed., 2009, 11, (4), pp. 581–588 (doi: 10.1109/TMM.2009.2017610).
-
27)
-
19. Jung, T.P., Makeig, S., Humphries, C., et al: ‘Removing electroencephalographics artifacts by blind source separation’, Soc. Psychophysiol. Res., 2000, 37, (2), pp. 163–178 (doi: 10.1111/1469-8986.3720163).
-
28)
-
6. Murat, Z.H., Shilawani, R., Kadir, S.A., Isa, R.M., Taib, M.N.: ‘The effects of mobile phone usage on human brainwave using EEG’. Int. Conf. on Modeling and Simulation, 2011, pp. 36–41.
-
29)
-
7. Panda, R., Khobragade, P.S., Jambhule, P.D., Jengthe, S.N., Pal, P.R., Gandhi, T.K.: ‘Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction’. Int. Conf. on Systems in Medicine and Biology, 2010, pp. 405–408.
-
30)
-
21. Ma, J., Tao, P., Bayram, S., Svetnik, V.: ‘Muscle artifacts in multichannel EEG: characteristics and reduction’, Clin. Neurophysiol., 2012, 123, (8), pp. 1676–1686 (doi: 10.1016/j.clinph.2011.11.083).
-
31)
-
26. Romo Vázquez, R., Vélez-Pérez, H., Ranta, R., et al: ‘Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling’, Biomed. Signal Process. Control, 2012, 7, (4), pp. 389–400 (doi: 10.1016/j.bspc.2011.06.005).
-
32)
-
31. Brabanter, K.D., Karsmakers, P., Ojeda, F., et al: .
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2013.0288
Related content
content/journals/10.1049/iet-spr.2013.0288
pub_keyword,iet_inspecKeyword,pub_concept
6
6