Volume 8, Issue 6 (November & December 2017)                   BCN 2017, 8(6): 479-492 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Hosseini S A. A Hybrid Approach Based on Higher Order Spectra for Clinical Recognition of Seizure and Epilepsy Using Brain Activity. BCN. 2017; 8 (6) :479-492
URL: http://bcn.iums.ac.ir/article-1-713-en.html
Research Center of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

Introduction: This paper proposes a reliable and efficient technique to recognize different epilepsy states, including healthy, interictal, and ictal states, using Electroencephalogram (EEG) signals.
Methods: The proposed approach consists of pre-processing, feature extraction by higher order spectra, feature normalization, feature selection by genetic algorithm and ranking method, and classification by support vector machine with Gaussian and polynomial radial basis function kernels. The proposed approach is validated on a public benchmark dataset to compare it with previous studies.
Results: The results indicate that the combined use of above elements can effectively decipher the cognitive process of epilepsy and seizure recognition. There are several bispectrum and bicoherence peaks at every bi-frequency plane, which reveal the location of the quadratic phase coupling. The proposed approach can reach, in almost all of the experiments, up to 100% performance in terms of sensitivity, specificity, and accuracy.
Conclusion: Comparing between the obtained results and previous approaches approves the effectiveness of the proposed approach for seizure and epilepsy recognition.

Type of Study: Original | Subject: Computational Neuroscience
Received: 2016/09/29 | Accepted: 2016/05/7 | Published: 2017/11/1

1. Abootalebi, V. (2000). [Higher order spectra study of EEG signal to assess hypnotizability (Persian)] (PhD dissertation). Tehran: Sharif University of Technology.
2. Adeli, H., Ghosh Dastidar, S., & Dadmehr, N. (2007). A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Transactions on Biomedical Engineering, 54(2), 205–11. doi: 10.1109/tbme.2006.886855 [DOI:10.1109/TBME.2006.886855]
3. Alam, S. M. S., & Bhuiyan, M. I. H. (2013). Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE Journal of Biomedical and Health Informatics, 17(2), 312–8. doi: 10.1109/jbhi.2012.2237409 [DOI:10.1109/JBHI.2012.2237409]
4. Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & 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. Physical Review, 64(6). doi: 10.1103/physreve.64.061907 [DOI:10.1103/PhysRevE.64.061907]
5. Berkovic, S. F., & Crompton, D. E. (2010). The borderland of epilepsy: A clinical and molecular view, 100 years on. Epilepsia, 51, 3–4. doi: 10.1111/j.1528-1167.2009.02432.x [DOI:10.1111/j.1528-1167.2009.02432.x]
6. Chandran, V., Acharya, R., & Lim, C. M. (2007). Higher order spectral (HOS) analysis of epileptic EEG signals. Paper Presented at the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 22-26 August 2007. doi: 10.1109/iembs.2007.4353847 [DOI:10.1109/IEMBS.2007.4353847]
7. Chisci, L., Mavino, A., Perferi, G., Sciandrone, M., Anile, C., Colicchio, G., et al. (2010). Real-time epileptic seizure prediction using AR models and support vector machines. IEEE Transactions on Biomedical Engineering, 57(5), 1124–32. doi: 10.1109/tbme.2009.2038990 [DOI:10.1109/TBME.2009.2038990]
8. Daliri, M. R. (2013). Kernel earth mover's distance for eeg classification. Clinical EEG and Neuroscience, 44(3), 182-7. doi: 10.1177/1550059412471521 [DOI:10.1177/1550059412471521]
9. Dekker, P. A., & World Health Organization. (2002). Epilepsy: A manual for medical and clinical officers in Africa. Geneva: World Health Organization.
10. Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. New Jersey: John Wiley & Sons.
11. Engel, J. (2006). ILAE classification of epilepsy syndromes. Epilepsy Research, 70, 5–10. doi: 10.1016/j.eplepsyres.2005.11.014 [DOI:10.1016/j.eplepsyres.2005.11.014]
12. Farooq, O., & Khan, Y. U. (2010). Automatic seizure detection using higher order moments. Paper presented at the International Conference on Recent Trends in Information, Telecommunication and Computing (ITC), Kochi, Kerala, India, 12-13 March 2010. doi: 10.1109/itc.2010.29 [DOI:10.1109/ITC.2010.29]
13. Fathima, T., Khan, Y. U., Bedeeuzzaman, M., & Farooq, O. (2011). Discriminant analysis for epileptic seizure detection. 2011 International Conference on Devices and Communications (ICDeCom), 24-25 February 2011. doi: 10.1109/icdecom.2011.5738454 [DOI:10.1109/ICDECOM.2011.5738454]
14. Fu, K., Qu, J., Chai, Y., & Zou, T. (2015). Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals. Biomedical Signal Processing and Control, 18, 179–85. doi: 10.1016/j.bspc.2015.01.002 [DOI:10.1016/j.bspc.2015.01.002]
15. Ghosh Dastidar, S., Adeli, H., & Dadmehr, N. (2007). Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Transactions on Biomedical Engineering, 54(9), 1545–51. doi: 10.1109/tbme.2007.891945 [DOI:10.1109/TBME.2007.891945]
16. Gotman, J. (1982). Automatic recognition of epileptic seizures in the EEG. Electroencephalography and clinical neurophysiology, 54(5), 530–40. doi: 10.1016/0013-4694(82)90038-4 [DOI:10.1016/0013-4694(82)90038-4]
17. Güler, İ., & Übeyli, E. D. (2004). Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction. Expert Systems with Applications, 27(3), 323–30. doi: 10.1016/j.eswa.2004.05.001 [DOI:10.1016/j.eswa.2004.05.001]
18. Guo, L., Rivero, D., Dorado, J., Rabu-al, J. R., & Pazos, A. (2010). Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. Journal of Neuroscience Methods, 191(1), 101–9. doi: 10.1016/j.jneumeth.2010.05.020 [DOI:10.1016/j.jneumeth.2010.05.020]
19. Harikumar, R., Raghavan, S., & Sukanesh, R. (2005). Genetic algorithm for classification of epilepsy risk levels from EEG signals. Paper presented at the TENCON 2005 - 2005 IEEE Region 10 Conference, Melbourne, Australia, 21-24 November 2005. doi: 10.1109/tencon.2005.300894 [DOI:10.1109/TENCON.2005.300894]
20. Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms. New Jersey: John Wiley & Sons.
21. Hinich, M. J. (1982). Testing for gaussianity and linearity of a stationary time series. Journal of Time Series Analysis, 3(3), 169–76. doi: 10.1111/j.1467-9892.1982.tb00339.x [DOI:10.1111/j.1467-9892.1982.tb00339.x]
22. Hosseini, S. A. (2009). [Quantification of EEG signals for evaluation of emotional stress level (Persian)] (MSc. dissertation). Mashhad: Islamic Azad University Mashhad Branch.
23. Hosseini, S. A., Akbarzadeh Totonchi, M. R., & Naghibi Sistani, M. B. (n.d.). Methodology for epilepsy and epileptic seizure recognition using chaos analysis of brain signals. Advances in Computational Intelligence and Robotics, 20–36. doi: 10.4018/978-1-4666-4038-2.ch002 [DOI:10.4018/978-1-4666-4038-2.ch002]
24. Hosseini, S. A., Akbarzadeh Totonchi, M. R., & Naghibi Sistani, M. B. (2015) . [Epilepsy recognition using chaotic qualitative and quantitative evaluation of EEG signals (Persian)]. Advances in Cognitive Science, 17(2), 43.
25. Hosseini, S. A., Akbarzadeh, T. M. R., & Naghibi Sistani, M. B. (2013). Qualitative and quantitative evaluation of EEG signals in epileptic seizure recognition. International Journal of Intelligent Systems and Applications, 5(6), 41–6. doi: 10.5815/ijisa.2013.06.05 [DOI:10.5815/ijisa.2013.06.05]
26. Hosseini, S. A., Khalilzadeh, M. A., Naghibi Sistani, M. B., & Niazmand, V. (2010). Higher order spectra analysis of EEG signals in emotional stress states. Paper Presented at the 2nd International Conference on Information Technology and Computer Science, Kiev, Ukraine, 24-25 July 2010. [DOI:10.1109/ITCS.2010.21]
27. Karayiannis, N. B., Mukherjee, A., Glover, J. R., Ktonas, P. Y., Frost, J. D., Hrachovy, R. A., et al. (2006). Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network. IEEE Transactions on Biomedical Engineering, 53(4), 633–41. doi: 10.1109/tbme.2006.870249 [DOI:10.1109/TBME.2006.870249]
28. Ktonas, P. Y. (1987). Automated spike and sharp wave (SSW) detection: Methods of analysis of brain electrical and magnetic signals. Amsterdam: Elsevier Science Publishers. [PMID]
29. Liang, S. F., Wang, H. C., & Chang, W. L. (2010). Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP Journal on Advances in Signal Processing, 2010(1):1-15. doi: 10.1155/2010/853434 [DOI:10.1155/2010/853434]
30. Lima, C. A. M., & Coelho, A. L. V. (2011). Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study. Artificial Intelligence in Medicine, 53(2), 83–95. doi: 10.1016/j.artmed.2011.07.003 [DOI:10.1016/j.artmed.2011.07.003]
31. López Cuevas, A., Castillo Toledo, B., Medina Ceja, L., Ventura Mejía, C., & Pardo Pe-a, K. (2013). An algorithm for on-line detection of high frequency oscillations related to epilepsy. Computer Methods and Programs in Biomedicine, 110(3), 354–60. doi: 10.1016/j.cmpb.2013.01.014 [DOI:10.1016/j.cmpb.2013.01.014]
32. Murro, A. M., King, D. W., Smith, J. R., Gallagher, B. B., Flanigin, H. F., & Meador, K. (1991). Computerized seizure detection of complex partial seizures. Electroencephalography and Clinical Neurophysiology, 79(4), 330–33. doi: 10.1016/0013-4694(91)90128-q [DOI:10.1016/0013-4694(91)90128-Q]
33. Musselman, M., & Djurdjanovic, D. (2012). Time–frequency distributions in the classification of epilepsy from EEG signals. Expert Systems with Applications, 39(13), 11413–22. doi: 10.1016/j.eswa.2012.04.023 [DOI:10.1016/j.eswa.2012.04.023]
34. Nigam, V. P., & Graupe, D. (2004). A neural-network-based detection of epilepsy. Neurological Research, 26(1), 55–60. doi: 10.1179/016164104773026534 [DOI:10.1179/016164104773026534]
35. Nikias, C. L., & Mendel, J. M. (1993). Signal processing with higher-order spectra. IEEE Signal Processing Magazine, 10(3), 10–37. doi: 10.1109/79.221324 [DOI:10.1109/79.221324]
36. Niknazar, M., Mousavi, S. R., Motaghi, S., Dehghani, A., Vosoughi Vahdat, B., Shamsollahi, M. B., et al. (2013). A unified approach for detection of induced epileptic seizures in rats using ECoG signals. Epilepsy & Behavior, 27(2), 355–64. doi: 10.1016/j.yebeh.2013.01.028 [DOI:10.1016/j.yebeh.2013.01.028]
37. Ocak, H. (2009). Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Systems with Applications, 36(2), 2027–36. doi: 10.1016/j.eswa.2007.12.065 [DOI:10.1016/j.eswa.2007.12.065]
38. Rana, P., Lipor, J., Hyong Lee, van Drongelen, W., Kohrman, M. H., & Van Veen, B. (2012). Seizure detection using the phase-slope index and multichannel ECoG. IEEE Transactions on Biomedical Engineering, 59(4), 1125–34. doi: 10.1109/tbme.2012.2184796 [DOI:10.1109/TBME.2012.2184796]
39. Shahid, S., Walker, J., Lyons, G. M., Byrne, C. A., & Nene, A. V. (2005). Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential. IEEE Transactions on Biomedical Engineering, 52(7), 1195–209. doi: 10.1109/tbme.2005.847525 [DOI:10.1109/TBME.2005.847525]
40. Srinivasan, V., Eswaran, C., & Sriraam, and N. (2005). Artificial neural network based epileptic detection using time-domain and frequency-domain features. Journal of Medical Systems, 29(6), 647–60. doi: 10.1007/s10916-005-6133-1 [DOI:10.1007/s10916-005-6133-1]
41. Srinivasan, V., Eswaran, C., & Sriraam, N. (2007). Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Transactions on Information Technology in Biomedicine, 11(3), 288–95. doi: 10.1109/titb.2006.884369 [DOI:10.1109/TITB.2006.884369]
42. Subasi, A. (2007). Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction. Computers in Biology and Medicine, 37(2), 227–44. doi: 10.1016/j.compbiomed.2005.12.003 [DOI:10.1016/j.compbiomed.2005.12.003]
43. Suykens, J. A. K., & Vandewalle, J. (2002). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300. doi: 10.1023/a:1018628609742 [DOI:10.1023/A:1018628609742]
44. Suykens, T., Van Gestel, J., De Brabanter, B., & De Moor, J. (2002). Vandewalle, least squares support vector machines. Singapore: World Scientific. [DOI:10.1142/5089]
45. Swami, A., Mendel, J. M., & Nikias, C. L. (2003). HOSA—higher order spectral analysis toolbox. Natick: MATLAB Central.
46. Tezel, G., & özbay, Y. (2009). A new approach for epileptic seizure detection using adaptive neural network. Expert Systems with Applications, 36(1), 172–80. doi: 10.1016/j.eswa.2007.09.007 [DOI:10.1016/j.eswa.2007.09.007]
47. Thomas, E. M., Temko, A., Marnane, W. P., Boylan, G. B., & Lightbody, G. (2013). Discriminative and generative classification techniques applied to automated neonatal seizure detection. IEEE Journal of Biomedical and Health Informatics, 17(2), 297–304. doi: 10.1109/jbhi.2012.2237035 [DOI:10.1109/JBHI.2012.2237035]
48. Tzallas, A. T., Tsipouras, M. G., & Fotiadis, D. I. (2007). Automatic seizure detection based on time-frequency analysis and artificial neural networks. Computational Intelligence and Neuroscience, 2007, 1–13. doi: 10.1155/2007/80510 [DOI:10.1155/2007/80510]
49. Tzallas, A. T., Tsipouras, M. G., & Fotiadis, D. I. (2009). Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Transactions on Information Technology in Biomedicine, 13(5), 703–10. doi: 10.1109/titb.2009.2017939 [DOI:10.1109/TITB.2009.2017939]
50. World Health Organization. (2017). Epilepsy. Geneva: World Health Organization.
51. Xiang, Y., & Tso, S. (2002). Detection and classification of flaws in concrete structure using bispectra and neural networks. NDT & E International, 35(1), 19–27. doi: 10.1016/s0963-8695(01)00018-4 [DOI:10.1016/S0963-8695(01)00018-4]
52. Zandi, A. S., Javidan, M., Dumont, G. A., & Tafreshi, R. (2010). Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEE Transactions on Biomedical Engineering, 57(7), 1639–51. doi: 10.1109/tbme.2010.2046417 [DOI:10.1109/TBME.2010.2046417]

Add your comments about this article : Your username or Email:

Send email to the article author

© 2020 All Rights Reserved | Basic and Clinical Neuroscience

Designed & Developed by : Yektaweb