Automatic feature extraction using generalised autoregressive conditional heteroscedasticity model: An application to electroencephalogram classification

IET Signal Processing

Volumn 6, Issue 9, 2012, Pages 829-838

  Mihandoost, S ^a   Amirani, M ^a   Mazlaghani, M ^b   Mihandoost, A ^c  

^a URMIA UNIVERSITY   (Iran)

^b AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

^c ILAM UNIVERSITY   (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTROENCEPHALOGRAPHY; FEATURE EXTRACTION; IMAGE SEGMENTATION; MARKOV PROCESSES; NEUROLOGY; SIGNAL PROCESSING; STATISTICAL METHODS; WAVELET TRANSFORMS;

AUTOMATIC FEATURE EXTRACTION; AUTOMATIC SEIZURE DETECTIONS; ELECTROENCEPHALOGRAM SIGNALS; GENERALISED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY; MARGINAL DISTRIBUTION; MARKOV RANDOM FIELDS; MULTI-LAYER PERCEPTRON CLASSIFIERS; STATISTICAL CHARACTERISTICS;

BIOMEDICAL SIGNAL PROCESSING;

EID: 84879998904     PISSN: 17519675     EISSN: 17519683     Source Type: Journal    
DOI: 10.1049/iet-spr.2011.0338     Document Type: Article

References (30)

1. Wan, B., Dhakal, B., Qi, H., Zhu, X.: 'Multi-method synthesizing to detect and classify epileptic waves in EEG'. Proc. Fourth IEEE Int. Conf. Computer and Inf-ormation Technology, 2004
2. Adeli, H., Zhou, Z., Dadmehr, N.: 'Analysis of EEG records in an epileptic patient using wavelet transform', J. Neurosci. Methods, 2003, 123, pp. 69-87
3. Orosco, L., Garces Correa, A., Laciar, E.: 'Multiparametric detection of epileptic seizures using empirical mode decomposition of EEG records'. Proc. 32nd Annual Int. Conf. IEEE EMBS, 2010
4. Uü beyli, E.D.: 'Combined neural network model employing wavelet coefficients for EEG signals classification', Digit. Signal Process., 2009a, 19, pp. 297-308
5. Xu, Q., Zhou, H., Wang, Y., Huang, J.: 'Fuzzy support vector machine for classification of EEG signals using wavelet-based features', Mach. Eng. Phys., 2009, 31, pp. 858-865
6. Amirmazlaghani, M., Amindavar, H.: 'EMG signals denoising via Baysian wavelet shrinkage based on GARCH modeling'. IEEE Conf. ICASSP, 2009
7. Ocak, H.: 'Automatic the detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy', Expert Syst. Appl., 2009, 36, pp. 2027-2036
8. Zarjam, P., Mesbah, M., Boashash, B.: 'Detection of newborns EEG seizure using optimal features based on discrete wavelet transform'. Proc. Fourth IEEE Int. Conf. Acoustics Speech and Signal Processing, 2003, vol. 2, pp. 265-268
9. Subasi, A.: 'EEG signal classification using wavelet feature extraction and a mixture of expert model', Expert Syst. Appl., 2007, 32, pp. 1084-1093
10. Faul, S., Gregorcic, G., Boylan, G., Marnane, W., Lightbody, G., Connolly, S.: 'Gaussian process modeling of EEG for the detection of neonatal seizures', IEEE Trans. Biomed. Eng., 2007, 54, pp. 2115-2116
11. Uübeyli, E.D.: 'Statistics over features: EEG signals analysis', Comput. Biol. Med., 2009b, 39, pp. 733-741
12. Mousavi, S.R., Niknazar, M., Vosoughi Vahdat, B.: 'Epileptic seizure detection using AR model on EEG signals'. Proc. IEEE Conf., 2008
13. Bollerslev, T.: 'Generalized autoregressive condition heteroscedasticiy', J. Econ., 1986, 31, pp. 307-327
14. Kevin Wonga, K.F., Galkab, A., Yamashitad, O., Ozaki, T.: 'Modelling non-stationary variance in EEG time series by state space GARCH model', Comput. Biol. Med., 2006, 36, pp. 1327-1335
15. Amirmazlaghan, M., Amindavar, H., Moghaddamjoo, A.: 'Speckle suppression in SAR images 2-D GARCH model', IEEE Trans. Image Process., 2009, 18, pp. 250-259
16. Cheng, Q., Zhou, H., Cheng, J.: 'The Fisher-Markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data', IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, pp. 1217-1233
17. Foo, S.Y., Stuart, G., Harvey, B., Meyer-Baese, A.: 'Neural networkbased EKG pattern recognition', Eng. Appl. Artif. Intell., 2002, 15, pp. 253-260
18. Orhan, U., Hekim, M., Ozer, M.: 'EEG signals classification using the K-means clustering and a multilayer perceptron neural network model', Expert Syst. Appl. J., 2011, 38, pp. 13475-13481
19. Ming Ting, C., Salleh, S.H.-H., Zainuddin, Z.M., Bahar, A.: 'Spectral estimation of nonstationary EEG using particle filtering with application to event-related desynchronization (ERD)', IEEE Trans. Biomed. Eng., 2011, 58, pp. 321-331
20. Schloügl, A., Flotzinger, D., Pfurtscheller, G.: 'Adaptive autoregressive modeling used for single-trial EEG classification', Biomed. Tech., 1997, 42, pp. 162-167
21. Engle, R.F.: 'Autoregressive condition heteroscedasticiy with estimate of the variance of U.K. inflation', Econometica, 1982, 50, pp. 987-1008
22. Amirmazlaghani, M., Amindavar, H.: 'Two novel Bayesian multiscale approaches for speckle suppression in SAR images', IEEE Trans. Geosci. Remote Sens., 2010, 48, pp. 2980-2993
23. Choqueuse, V., Yao, K., Collin, L., Burel, G.: 'Blind detection of the number of communication signals under spatially correlated noise by ICA and K-S test'. IEEE Conf. ICASSP, 2008
24. Andrzejak, R., Lehnertz, G.K., Mormann, F., Rieke, C., David, P., Elger, C.E.: 'Indications of nonlinear detection and finite-dimensional structures in time series of brain electrical activity dependence on recording region and brain state', Phys. Rev. E, 2001, 64, (6), p. 061907
25. Institute of Medical Biometry, Information and Epidemiology of the 'Medizinische Einrichtungen der Universitaüt Bonn', Available at http://www.meb.uni-bonn.de/epileptology/science/physik/eeg.data.html
26. Soltani, S.: 'On the use of the wavelet decomposition for time series predication', Neurocomputing, 2002, 48, pp. 267-277
27. Oüzguür, A., Oüzguür, L., Guüngoür, T.: 'Text categorization with class-based and corpus-based keyword selection'. ISCIC Conf., 2005, pp. 606-615
28. Guo, L., Wu, Y., Zhao, L., Cao, T., Yan,W., Shen, X.: 'Classification of mental task from EEG signals using immune feature weighted support vector machines', IEEE Trans. Magn., 2011, 47, pp. 866-869
29. Srinivasan, V., Eswaran, C., Sriraam, N.: 'Artificial neural network based epileptic detection using time-domain and frequency-domain features', J. Med. Syst., 2005, 29, pp. 647-660
30. Tazallas, A., Tsipouras, M., Fotiadis, D.: 'A time-frequency based method for the detection of epileptic seizures in EEG recordings'. Proc. 20th IEEE Int. Symp. Computer-Based Medical Systems, CMS'07, 2007, pp. 135-140