Classification of epilepsy using high-order spectra features and principle component analysis

Journal of Medical Systems

Volumn 36, Issue 3, 2012, Pages 1731-1743

  Du, Xian ^a   Dua, Sumeet ^a,b   Acharya, Rajendra U ^c   Chua, Chua Kuang ^c  

^a Louisiana Tech University   (United States)

^b LOUISIANA STATE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

Author keywords

Classifiers; EEG; Epilepsy; High order spectra; Principle component analysis

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; CLINICAL ARTICLE; CONTROLLED STUDY; DISEASE CLASSIFICATION; ELECTROENCEPHALOGRAM; EPILEPSY; HUMAN; LOGISTIC REGRESSION ANALYSIS; NONLINEAR SYSTEM; PRINCIPAL COMPONENT ANALYSIS; RECEIVER OPERATING CHARACTERISTIC; SIGNAL DETECTION; SPECTROMETRY;

AREA UNDER CURVE; ELECTROENCEPHALOGRAPHY; EPILEPSY; HUMANS; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84864031840     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9633-6     Document Type: Article

References (51)

1. Acharya, U. R., Chua, C. K., Lim, T. C., Dorithy, and Suri, J. S., Automatic identification of epileptic EEG signals using nonlinear parameters. J. Mech. Med. Biol. 9(4):539-553, 2009.
2. Andrzejak, R. G., Lehnertz, K., Rieke, C., Mormann, F., David, P., and Elger, C. E., 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, 2001.
3. Aschenbrenner-Scheibe, R., Maiwald, T., Winterhalder, M., Voss, H. U., Timmer, J., and Schulze-Bonhage, A., How well can epileptic seizures be predicted? An evaluation of a nonlinear method. Brain 12:2616-2626, 2003. (Pubitemid 37463081)
4. Babloyantz, A., and Destexhe, A., Low-dimensional chaos in an instance of epilepsy. Proc Nat'l Acad Sci USA 18(10):3513-3517, 1986. (Pubitemid 16043065)
5. Breiman, L., Random Forests. Mach Learn 45:5-32, 2001. (Pubitemid 32933532)
6. Cataltepe, Z., Genc, H. M., and Pearson, T., A PCA/ICA based feature selection method and its application for corn fungi detection, 15th European Signal Processing Conference (EUSIPCO 2007). EURASIP, Poznan, pp. 970-974, 2007.
7. Chua, K. C., Chandran, V., Acharya, U. R., and Lim, C. M., Higher order spectral (HOS) analysis of epileptic EEG signals, 29th IEEE-EMBS-2007, Lyon, 6495-6498, 2007.
8. Chua, K. C., Chandran, V., Acharya, U. R., and Lim, C. M., Automatic identification of epileptic electroencephalography signals using higher-order spectra. Proc Inst Mech Eng H J Eng Med 223(4):485-495, 2009.
9. Chua, K. C., Chandran, V., Acharya, U. R., Lim, C. M., Application of higher order spectra to identify epileptic EEG. J. Med. Syst., 2010, (In Press).
10. Chua, K. C., Chandran, V., Acharya, U. R., and Lim, C. M., Analysis of epileptic EEG signals using higher order spectra. J Med Eng Technol UK 33(1):42-50, 2009.
11. Cockerell, O. C., Hart, Y. M., Sander, J. W. A. S., Goodridge, D. M. G., Shorvon, S. D., and Johnson, A. L., Mortality from epilepsy: Results from a prospective population-based study. Lancet 344:918-921, 1994. (Pubitemid 24295878)
12. EEG time series database. http://www.meb.unibonn.de/epileptologie/ science/physik/eegdata
13. Acharya, U. R., Chua, E. C. P., Chua, K. C., Lim, C. M., and Tamura, T., Analysis and automatic identification of sleep stages using higher order spectra. Int J Neural Syst 20(6):509-521, 2010.
14. Faust, O., Acharya, U. R., and Lim, C. M., Automatic identification of epileptic and background EEG signals using frequency domain parameters. Int J Neural Syst 20(2):159-176, 2010.
15. Frank, E., Wang, Y., Inglis, S., Holmes, G., and Witten, I. H., Using model trees for classification. Mach Learn 32(1):63-76, 1998.
16. Freeman, W. J., Simulation of chaotic EEG patterns with a dynamic model of the olfactory system. Biol Cybern 56:139-150, 1987. (Pubitemid 17093973)
17. Golub, G. H., and Van Loan, C. F., Matrix computations. Johns Hopkins University Press, Baltimore, 1989.
18. Guler, N. F., Ubey, E. D., and Guler, I., Recurrent neural network employing Lyapunovexponents for EEG signals classification. Exp Syst Appl 29(3):506-514, 2005. (Pubitemid 41230775)
19. Hosmer, D. W., and Lemeshow, S., Applied Logistic Regression. Wiley, New York, 1989.
20. Iasemidis, L. D., Zaveri, H. P., Sachellares, J. C., and Williams, W. J., Linear and nonlinear modeling of ECoG in temporal lobe epilepsy. Proceedings of the 25th annual rocky mountain bioengineering symposium 24:187-193, 1988.
21. Rodriguez, J. J., Kuncheva, L. I., and Alonso, C. J., Rodriguez, Ludmila I. Kuncheva, and Carlos J. Alonso, "Rotation Forest: A New Classifier Ensemble Method". IEEE Trans Pattern Anal Mach Intell 28:1619-1620, 2006.
22. Landwehr, N., Hall, M., and Frank, E., Logistic model trees. Mach Learn 59:161-205, 2005. (Pubitemid 40890416)
23. Kannathal, N., Lim, C. M., Acharya, U. R., and Sadasivan, P. K., Entropies for detection of epilepsy in EEG. Comput Methods Programs Biomed 80(3):187-194, 2005. (Pubitemid 41619331)
24. Kannathal, N., Acharya, U. R., Lim, C. M., and Sadasivan, P. K., Characterization of EEG-A comparative study. Comp Meth Prog Biomed 80(1):17-23, 2005.
25. Ya Kaplan, A., and Shishkin, S. L., Application of the changepoint analysis to the investigation of the brain's electrical activity, [book auth.]. In: Brodsky, B. E., and Darkhovsky, B. S. (Eds.), Nonparametric statistical disgnosis: Problems and methods. Kluwer Academic Publishers, Dordrecht, pp. 333-388, 2000.
26. Lagerlund, T. D., Sharbrough, F. W., and Busacker, N. E., Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular velue decomposition. J Clin Neurophysiol 14(1):73-82, 1997. (Pubitemid 27220507)
27. Li, G. Z., Bu, H. L., Yang, M. Q., Zeng, X. Q., and Yang, J. Y., "Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis" IEEE 7th International Conference on Bioinformatics and Bioengineering at Harvard Medical School. BMC Genomics, Boston, pp. 14-17, 2008.
28. Lange, H. H., Lieb, J. P., Engel, J., Jr., and Crandall, P. H., Temporo-spatial paterns of preictal spike activity in human temporal lobe epilepsy. Electroencephalogr Clin Neurophysiol 56:543-555, 1983. (Pubitemid 14218271)
29. Lotte, F., Congedo, M., Lecuyer, A., Lamarche, F., and Arnaldi, B., A review of classification algorithms for EEG-based braincomputer interfaces. J Neural Eng 4:R1-R13, 2007. (Pubitemid 46603810)
30. Malhi, A., and Gao, R. X., PCA-based feature selection scheme for machine defect classification. IEEE Trans Instrum Meas 53:1517-1525, 2004.
31. Martinerie, J., Adam, C., Quyen, M. L. V., Baulac, M., Clemenceau, S., Renault, B., and Varela, F. J., Epileptic seizures can be anticipated by non-linear analysis. Nature Med 4(10):1173-1176, 1998. (Pubitemid 28468831)
32. Mayer-Kress, G., and Layne, S. P., Dimensionality of the human electroencephalogram. Ann NY Acad Sci 504:62-87, 1987.
33. Mohoney, C. M. W., and Drineas, P., Unsupervised feature selection for principal component analysis boutsidis. KDD'08, Las Vegas, Nevada: ACM, 2008, pp. 61-69.
34. Mormann, F., Thomas, K., Chrisophy, R., Andrzejak, R., Kraskov, A., David, P., Elger, C. E., and Lehnertz, K., On the predictability of epileptic seizures. Clin Neurophysiol 116:569-587, 2005. (Pubitemid 40249825)
35. Mormann, F., Andrzejak, R. G., Elger, C. E., andLehnertz, K., Seizure prediction: The long and winding road. Brain 130:314-333, 2007. (Pubitemid 46181087)
36. Ng, T. T., Chang, S. F., Sun,Q., Blind detection of photomontage using higher order statistics. IEEE Int. Symp. Circuits Syst. (ISCAS), 2004.
37. Nigam, V. P., and Graupe, D., A neural-network-based detection of epilepsy. Neurol Res 26(6):55-60, 2004. (Pubitemid 38174388)
38. Pijn, J. P., Neerven, J. V., Noest, A., and Silva, F. L., Chaos or noises in EEG signals; dependence on state and brain site. Electroencephalogr Clin Neurophysiol 79:371-381, 1991.
39. Polat, K., and Guenes, S., Classification of epileptiform EEG using a hybrid systems based on decision tree classifier and fast fourier transform. Appl Math Comput 32(2):625-631, 2007.
40. Quinlan, R. J., "Learning with Continuous Classes" 5th Australian Joint Conference on Artificial Intelligence, pp. 343-348, 1992.
41. Sadati, N., Mohseni, H. R., and Magshoudi, A., Epileptic Seizure Detection Using Neural Fuzzy Networks. Proc. IEEE Int. Conf. Fuzzy Syst., Canada 596-600, 2006. (Pubitemid 46948816)
42. Shoeb, A. H., "Application of machine learning to epileptic seizure onset detection and treatment", MIT PhD thesis, 2009. http://dspace.mit. edu/handle/1721.1/54669
43. Skarda, C. A., and Freeman, W. J., How brains make chaos in order to make sense of the world. Behav Brain Sci 19:161-195, 1987.
44. Srinivasan, V., Eswaran, C., and Sriraam, N., Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed 11(3):288-295, 2007. (Pubitemid 46758846)
45. Subasi, A., EEG signal classification using wavelet feature and a mixture of expert model. Expert Syst Appl 32(4):1084-1093, 2006.
46. Subasi, A., Signal classification using wavelet feature extraction and a mixture of expert model. Exp Syst Appl 32(4):1084-1093, 2007. (Pubitemid 44821797)
47. Subasia, A., and Ercelebi, E., Classification of EEG signals using neural network and logistic regression. Comput Methods Programs Biomed 78:87-99, 2005. (Pubitemid 40575687)
48. Wang, Y., and Witten, I. H., Induction of model trees for predicting continuous classes. Poster papers of the 9th European Conference on Machine Learning, 1997.
49. Witten, I. H., and Frank, E., Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005.
50. Zhang, J., Zheng, C., Jiang, D., et al., Bispectrum analysis of focal ischemic cerebral EEG signal. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 20:2023-2026, 1998.
51. Zhou, S. M., Gan, J. Q., and Sepulveda, F., Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface. Inf Sci 178(6):1629-1640, 2008.