[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.
|