A review of developments of EEG-based automatic medical support systems for epilepsy diagnosis and seizure detection
Yuedong Song
DOI: 10.4236/jbise.2011.412097   PDF    HTML     8,162 Downloads   14,682 Views   Citations

Abstract

Epilepsy is one of the most common neurological disorders-approximately one in every 100 people worldwide are suffering from it. The electroencephalogram (EEG) is the most common source of information used to monitor, diagnose and manage neurological disorders related to epilepsy. Large amounts of data are produced by EEG monitoring devices, and analysis by visual inspection of long recordings of EEG in order to find traces of epilepsy is not routinely possible. Therefore, automated detection of epilepsy has been a goal of many researchers for a long time. Until now, reviews of epileptic seizure detection have been published but none of them has specifically reviewed developments of automatic medical support systems utilized for EEG-based epileptic seizure detection. This review aims at filling this lack. The main objective of this review will be to briefly discuss different methods used in this research field and describe their critical properties.

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Song, Y. (2011) A review of developments of EEG-based automatic medical support systems for epilepsy diagnosis and seizure detection. Journal of Biomedical Science and Engineering, 4, 788-796. doi: 10.4236/jbise.2011.412097.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Iasemidis, L.D., Shiau, D.S., Chaovalitwongse, W., Sackellares, J.C., Pardalos, P.M., Principe, J.C., Carney, P.R., Prasad, A., Veeramani, B. and Tsakalis, K. (2003) Adaptive epileptic seizure prediction system. IEEE Transaction on Biomedical Engineering, 50, 616-627. doi:10.1109/TBME.2003.810689
[2] Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P. and 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 Reviews E, 64, 061907. doi:10.1103/PhysRevE.64.061907
[3] IFSECN (1974) A glossary of terms most commonly used by clinical electroencephalographers. Electroencephalography and Clinical Neurophysiology, 37, 538-548. doi:10.1016/0013-4694(74)90099-6
[4] Gotman, J., Flanagan, D., Zhang, J. and Rosenblatt, B. (1997) Automatic seizure detection in the newborn: Methods and initial evaluation. Electroencephalography and Clinical Neurophysiology, 103, 356-362. doi:10.1016/S0013-4694(97)00003-9
[5] Polat, K. and Günes, S. (2007) Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast fourier transform. Applied Mathematics and Computation, 187, 1017-1026. doi:10.1016/j.amc.2006.09.022
[6] übeyli, E.D. (2009) Combined neural network model employing wavelet coefficients for EEG signals classification. Digital Signal Processing, 19, 297-308.
[7] Subasi, A. (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications, 32, 1084-1093. doi:10.1016/j.eswa.2006.02.005
[8] Zandi, A.S., Javidan, M., Dumont, G.A. and 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, 1639-1651. doi:10.1109/TBME.2010.2046417
[9] Adeli, H., Zhou, Z. and Dadmehr, N. (2003) Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods, 123, 69-87. doi:10.1016/S0165-0270(02)00340-0
[10] Srinivasan, V., Eswaran, C. and Sriraam, N. (2005) Artificial neural network based epileptic detection using time-domain and frequency-domain features. Journal of Medical Systems, 29, 647-660. doi:10.1007/s10916-005-6133-1
[11] Saab, M.E. and Gotman, J. (2005) A system to detect the onset of epileptic seizures in scalp EEG. Clinical Neurophysiology, 116, 427-442. doi:10.1016/j.clinph.2004.08.004
[12] Indiradevi, K.P., Elias, E., Sathidevi, P.S., Dinesh, S. and Radhakrishnan, K. (2008) A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram. Computers in Biology and Medicine, 38, 805-816. doi:10.1016/j.compbiomed.2008.04.010
[13] übeyli, E.D. (2009) Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents. Expert Systems with Applications, 36, 9031-9038. doi:10.1016/j.eswa.2008.12.019
[14] Ghosh-Dastidar, S., Adeli, H. and Dadmehr, N. (2007) Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Transactions on Biomedical Engineering, 54, 1545-1551. doi:10.1109/TBME.2007.891945
[15] Kannathal, N., Choo, M.L., Rajendra, A.U. and Sadasivan, P.K. (2005) Entropies for detection of epilepsy in EEG. Computer Method and Programs in Biomedicine, 80, 187-194. doi:10.1016/j.cmpb.2005.06.012
[16] Ocak, H. (2008) Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Signal Processing, 88, 1858-1867. doi:10.1016/j.sigpro.2008.01.026
[17] Srinivasan, V., Eswaran, C. and Sriraam, N. (2007) Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Transactions on Information Technology in Biomedicine, 11, 288-295. doi:10.1109/TITB.2006.884369
[18] Orhan, U., Hekim, M., Ozer, M. (2011) EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Systems with Applications, 38, 13475-13481. doi:10.1016/j.eswa.2011.04.149
[19] Kumar, S.P., Sriraam, N., Benakop, P.G. and Jinaga, B.C. (2009) Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Systems with Applications, 37, 3284-3294. doi:10.1016/j.eswa.2009.09.051
[20] Naghsh-Nilchi, A.R. and Aghashahi, M. (2010) Epilepsy seizure detection using eigen-system spectral estimation and multiple layer perceptron neural network. Biomedical Signal Processing and Control, 5, 147-157. doi:10.1016/j.bspc.2010.01.004
[21] Kocyigit, Y., Alkan, A. and Erol, H. (2008) Classification of EEG recordings by using fast independent component analysis and artificial neural network. Journal of Medical Systems, 32, 17-20. doi:10.1007/s10916-007-9102-z
[22] Guo, L., Rivero, D., Dorado, J., Rabunal, J.R. and Pazos, A. (2010) Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. Journal of Neuroscience Methods, 191, 101-109. doi:10.1016/j.jneumeth.2010.05.020
[23] Boser, B.E. (1992) A training algorithm for optimal margin classifiers. Proceedings of 5th Annual Workshop of Computational Learning Theory, Pennsylvania, 144-152.
[24] Cortes, C. and Vapnik, V. (1995) Support vector networks. Machine Learning, 20, 273-297. doi:10.1007/BF00994018
[25] Guler, I. and übeyli, E.D. (2007) Multiclass Support Vector Machines for EEG-Signals Classification. IEEE Transactions on Information Technology in Biomedicine, 11, 117-126. doi:10.1109/TITB.2006.879600
[26] Gardner, A.B., Krieger, A.M., Vachtsevanos, G. and Litt, B. (2006) One-class novelty detection for seizure analysis from intracranial EEG. Journal of Machine Learning Research, 7, 1025-1044.
[27] Hsu, K.C. abd Yu, S.N. (2010) Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm. Computers in Biology and Medicine, 40, 823- 830. doi:10.1016/j.compbiomed.2010.08.005
[28] Chandaka, S., Chatterjee, A. and Munshi, S. (2009) Cross- correlation aided support vector machine classifier for classification of EEG signals. Expert Systems with Applications, 36, 1329-1336. doi:10.1016/j.eswa.2007.11.017
[29] übeyli, E.D. (2008) Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines. Computers in Biology and Medicine, 38, 14-22. doi:10.1016/j.compbiomed.2007.06.002
[30] Alkan, A., Koklukaya, E. and Subasi, A. (2005) Automatic seizure detection in EEG using logistic regression and artificial neural network. Journal of Neuroscience Methods, 148, 167-176. doi:10.1016/j.jneumeth.2005.04.009
[31] Chaovalitwongse, W.A., Fan, Y. and Sachdeo, E.C. (2007) On the time series k-nearest neighbour classification of abnormal brain activity. IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, 37, 1005-1016. doi:10.1109/TSMCA.2007.897589
[32] Aarabi, A., Fazel-Rezai, R. and Aghakhani, Y. (2009) A fuzzy rule-based system for epileptic seizure detection in intracranial EEG. Clinical Neurophysiology, 120, 1648-1657. doi:10.1016/j.clinph.2009.07.002
[33] Tito, M., Cabrerizo, M., Ayala, M., Barreto, A., Miller, I, Jayakar, P. and Adjouadi, M. (2009) Classification of electroencephalographic seizure recordings into ictal and interictal files using correlation sum. Computers in Biology and Medicine, 39, 604-614. doi:10.1016/j.compbiomed.2009.04.005
[34] Zavar, M., Rahati, S., Akbarzadeh-T, M.-R. and Ghasemifard, H. (2011) Evolutionary model selection in a wave- let-based support vector machine for automated seizure detection. Expert Systems with Applications, 38, 10751- 10758. doi:10.1016/j.eswa.2011.01.087
[35] Zandi, A.S., Javidan, M., Dumont, G.A. and Tafreshi, R. (2010) Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wave-let packet transform. IEEE Transactions on Biomedical Engineering, 57, 1639-1651. doi:10.1109/TBME.2010.2046417
[36] Iasemidis, L.D., Shiau, D.S., Sackellares, J.C., Pardalos, P.M. and Prasad A. (2004) A dynamical resetting of the human brain at epileptic seizures: Application of nonlinear dynamics and global optimization techniques. IEEE Transactions on Biomedical Engineering, 51, 493-506. doi:10.1109/TBME.2003.821013
[37] Iasemidis, L.D., Sackellares, J.C., Zaveri, H.P. and Willians, W.J. (1990) Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures. Brain Topography, 2, 187-201. doi:10.1007/BF01140588
[38] Lehnertz, K. and Elger, C.E. (1995) Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. Elec- troencephalogram Clinical Neurophysiology, 95, 108-117. doi:10.1016/0013-4694(95)00071-6
[39] Lerner, D.E. (1996) Monitoring changing dynamics with correlation integrals: Case study of an epileptic seizure. Physica D, 97, 563-576. doi:10.1016/0167-2789(96)00085-1
[40] Osorio, I., Harrison, M.A.F., Lai, Y.C. and Frei, M.G. (2001) Observations on the application of the correlation dimension and correlation integral to the prediction of seizures. Journal of Clinical Neurophysiology, 18, 269-274. doi:10.1097/00004691-200105000-00006
[41] Van Quyen, M.L., Martinerie, J., Baulac, M. and Varela, F.J. (1999) Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings. NeuroReport, 10, 2149-2155. doi:10.1097/00001756-199907130-00028
[42] Litt, B., Estellera, R., Echauz, J., D’Alessandro, M., Shor, R., Henry, T., Pennell, P., Epstein, C., Bakay, R., Dichter, M. and Vachtsevanos, G. (2001) Epileptic seizures may begin hours in advance of clinical onset: A report of five patients. Neuron, 30, 51-64. doi:10.1016/S0896-6273(01)00262-8
[43] D’Alessandro, M., Esteller, R., Vachtsevanos, G., Hinson, A., Echauz J. and Litt B. (2003) Epileptic seizure prediction using hybrid feature selection over multiple intracranial eeg electrode contacts: A report of four patients. IEEE Transactions on Biomedical Engineering, 50, 603- 615. doi:10.1109/TBME.2003.810706
[44] Moser, H.R., Weber, B., Wieser, H.G. and Meier, P.F. (1999) Electroencephalogram in epilepsy: Analysis and seizure prediction within the framework of Lyapunov theory. Physica D, 130, 291-305. doi:10.1016/S0167-2789(99)00043-3
[45] Hively, L.M., Protopopescu, V.A. and Gailey, P.C. (2000) Timely detection of dynamical change in scalp EEG signals. Chaos, 10, 864-875. doi:10.1063/1.1312369
[46] Hively, L.M. and Protopopescu, V.A. (2003) Channel-consistent forewarning of epileptic events from scalp EEG. IEEE Transactions on Biomedical Engineering, 50, 584- 593. doi:10.1109/TBME.2003.810693
[47] Sackellares, J., Iasemidis, L., Shiau, D., Gilmore, R. and Roper, S. (1999) Detection of the preictal transition from scalp EEG recordings. Epilepsia, 40, 176.
[48] Shiau, D., Iasemidis, L., Suharitdamrong, W., Dance, L., Chaovalitwongse, W., Pardalos, P., Carney, P. and Sackellares, J. (2003) Detection of the preictal period by dynamical analysis of scalp EEG. Epilepsia, 44, 233-234.

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