___

• Adeli, H., Zhou, Z., ve Dadmehr, N., “Analysis
• of EEE Records in an Epileptic Patient using Wavelet Transform”, Journal of Neuroscience
• Methods, No 123, 69–87, 2003.
• Güler, İ., ve Übeyli, E. D., “Adaptive Neuro-
• Fuzzy Inference System for Classification of
• EEG Signals using Wavelet Coefficients”,
• Journal of Neuroscience Methods, No 148,
• –121, 2005.
• Khan, Y. U., ve Gotman, J., “Wavelet Based
• Automatic Seizure Detection in Intra-cerebral
• Electroencephalogram”, Clinical Neurophysiology,
• No 114, 898–908, 2003.
• Kıymık, M. K., Akın, M., ve Subaşı, A.,
• “Automatic Recognition of Alertness Level by
• using Wavelet Transform and Artificial Neural
• Network”, Journal of Neuroscience Methods,
• No 139, 231–240, 2004.
• Ocak, H., “Automatic Detection of Epileptic
• Seizures in EEG using Discrete Wavelet
• Transform and Approximate Entropy”, Expert
• Systems with Applications, No 36, 2027–2036,
• -
• Subaşı, A., “Automatic Recognition of Alertness
• Level from EEG by using Neural Network and
• Wavelet Coefficients”, Expert Systems with
• Applications, No 28, 701–711, 2005.
• Subaşı, A., “Epileptic Seizure Detection using
• Dynamic Wavelet Network”, Expert Systems
• with Applications, No 29, 343–355, 2005.
• Subaşı, A., “Automatic Detection of Epileptic
• Seizure using Dynamic Fuzzy Neural Networks”,
• Expert Systems with Applications, No 31, 320–
• , 2006.
• Subaşı, A., “EEG Signal Classification using
• Wavelet Feature Extraction and a Mixture of
• Expert Model”, Expert Systems with
• Applications, No 32, 1084–1093, 2007.
• Übeyli, E. D., “Combined Neural Network Model
• Employing Wavelet Coefficients for EEG Signals
• Classification”, Digital Signal Processing, No
• , 297–308, 2009.
• Übeyli, E. D., “Decision Support Systems for
• Time-Varying Biomedical Signals: EEG Signals
• Classification”, Expert Systems with
• Applications, No 36, 2275–2284, 2009.
• Alkan, A., Köklükaya, E., ve Subaşı, A.,
• “Automatic Seizure Detection in EEG using
• Logistic Regression and Artificial Neural
• Network”, Journal of Neuroscience Methods,
• No 148, 167–176, 2005.
• Subaşı, A., ve Ercelebi, E., “Classification of
• EEG Signals using Neural Network and Logistic
• Regression”, Computer Methods and
• Programs in Biomedicine, No 78, 87–99, 2005.
• Altunay, S., Telatar, Z., ve Eroğul, O., “Epileptic
• EEG Detection using the Linear Prediction Error
• Energy”, Expert Systems with Applications,
• Cilt 37, No 8, 5661–5665, 2010.
• Acır, N., “Automated System for Detection of
• Epileptiform Patterns in EEG by using a
• Modified RBFN Classifier”, Expert Systems
• with Applications, Cilt 29, No 2, 455–462, 2005.
• Subaşı, A., “Application of Adaptive Neuro-
• Fuzzy Inference System for Epileptic Seizure
• Detection using Wavelet Feature Extraction”,
• Computers in Biology and Medicine, Cilt 37,
• No 2, 227–244, 2007.
• Aslan, K., Bozdemir, H., Şahin, S., Oğulata, S. N.,
• ve Erol, R., “A Radial Basis Function Neural
• Network Model for Classification of Epilepsy
• using EEG Signals”, The Journal of Medical
• Systems, No 32, 403–408, 2008.
• Petrosian, A., Prokhorov, D., Homan, R., Dashei,
• R., ve Wunsch, D., “Recurrent Neural Network
• Based Prediction of Epileptic Seizures in Intra
• and Extra-cranial EEG”, Neurocomputing, No
• , 201–218, 2000.
• Srinivasan, V., Eswaran, C., ve Sriraam, N.,
• “Artificial Neural Network based Epileptic
• Detection using Time-Domain and Frequency-
• Domain Features”, Journal of Medical Systems,
• Cilt 29, No 6, 647–660, 2005.
• Pradhan, N., Sadasivan, P. K., ve Arunodaya, G.
• R., “Detection of Seizure Activity in EEG by an
• Artificial Neural Network: A Preliminary Study”,
• Computers and Biomedical Research, No 29,
• –313, 1996.
• Übeyli, E. D., “Analysis of EEG Signals by
• Combining Eigenvector Methods and Multi-class
• Support Vector Machines”, Computers in
• Biology and Medicine, Cilt 38, No 1, 14–22,
• -
• Kıymık, M. K., Subaşı, A., ve Özçalık, H. R.,
• “Neural Networks with Periodogram and
• Autoregressive Spectral Analysis Methods in
• Detection of Epileptic Seizure”, Journal of
• Medical Systems, Cilt 28, No 6, 511–522, 2004.
• Kıymık, M. K., Güler, İ., Dizibüyük, A., and
• Akın, M., “Comparison of STFT and Wavelet
• Transform Methods in Determining Epileptic
• Seizure Activity in EEG Signals for Real-time
• Application”, Computers in Biology and
• Medicine, Cilt 35, No 7, 603-616, 2005.
• Andrzejak, R. G., Lehnertz, K., Mormann, F.,
• Rieke, C., David, P., ve 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”, Physical Review E,
• Cilt 64, No 6, 061907, 2001.
• Hsu, C. N., Huang, H., J., Wong, T. T.,
• “Implications of the Dirichlet Assumption for
• Discretization of Continuous Variables in Naive
• Bayesian Classifiers”, Machine Learning, Cilt
• , No 3, 235–263, 2003.
• Jiang, S., Li, X., Zheng, Q., ve Wang, L.,
• “Approximate Equal Frequency Discretization
• Method”, Global Congress on Intelligent
• Systems, 514-518, 2009.
• Tay, E. H., ve Shen, L., “A Modified Chi2
• Algorithm for Discretization”, IEEE
• Transactions on Knowledge and Data
• Engineering, Cilt 14, No 3, 666–670, 2002.
• Gang, L. ve Tong, F., “An Unsupervised
• Discretization Algorithm based on Mixture
• Probabilistic Model”, Chinese Journal of
• Computers, Cilt 25, No 2, 158–164, 2002.
• Lee, C.H., “A Hellinger-based Discretization
• Method for Numeric Attributes in Classification
• Learning”, Knowledge-Based Systems, Cilt 20,
• No 4, 419–425, 2007.
• Fayyad, U.M, ve Irani, K.B., “Multi-interval
• Discretization of Continuous Valued Attributes
• for Classification Learning”, Proc. of the 13th
• International Joint Conference on Artificial
• Intelligence, 1022–1029, 1993.
• Clarke, E.J. ve Braton, B.A., “Entropy and MDL
• Discretization of Continuous Variables for
• Bayesian Belief Networks”, International
• Journal of Intelligence Systems, No 15, 61–92,
• -
• Xi, J. Ouyang, W.M., “Clustering based
• Algorithm for Best Discretizing Continuous
• Valued Attributes”, Mini-micro systems, Cilt 21,
• No 10, 1025–1027, 2000.