Malika D. Kedir-Talha and Saliha Ould Slimane
[1] H.R. Mohseni, A. Maghsoudi, and M.B. Shamsollahi, Seizure detection in EEG signals: a comparison of different approaches, Annual Int. of the IEEE Engineering in Medicine and Biology Society, 2006, 6724–6727. [2] R. Ceylan and Y. Ozbay, Comparison of FCM, PCA and WTtechniques for classification ECG arrhythmias using artificial neural network, Expert Systems with Applications, 33, 2007, 286–295. [3] I. Omerhodzic, S. Avdakovic, A. Nuhanovic, and K. Dizdarevic, Energy distribution of EEG signals: EEG signal wavelet-neural network classifier, International Journal of Biological and Life Sciences, 6, 2010, 210–215. [4] P.W. Mirowski, Y. LeCun, D. Madhavan, and R. Kuzniecky, Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG, IEEE Workshop on Machine Learning for Signal Processing, 2008, 244–249. [5] Y. ¨Ozbay, R. Ceylan, and B. Karlik, A fuzzy clustering neural network architecture for classification of ECG arrhythmias, Computers in Biology and Medicine, 36, 2006, 376–388. [6] A. Subasi and E. Ercelebi, Classification of EEG signals using neural network and logistic regression, Computer Methods and Programs in Biomedicine, 78, 2005, 87–99. [7] T. Froese, S. Hadjiloucas, K.H. Galv˜ao, V.M. Becerra, and C.J. Coelho, Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms, Pattern Recognition Letters, 27, 2006, 393–407. [8] S. Yu and Y. Chen, Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network,Pattern Recognition Letters, 28, 2007, 1142–1150. [9] H. Khorrami and M. Moavenian, A comparative study of DWT, CWT and DCT in ECG arrhythmias classification, Elsevier Expert Systems with Applications, 2010, 5751–5757. [10] S. Abe, Support vector machines for pattern classification (London: Springer-Verlag, 2005). [11] J.M. Roig, R.V. Galiano, F.J. Chorro-Gasco, and A. Cebrian, Support vector machine for arrhythmia discrimination with wavelet transform based feature selection, Computers in Cardiology, 27, 2000, 407–410. [12] E. Ubeyli, Support vector machines for detection of electrocardiographic changes in partial epileptic patients, Engineering Applications of Artificial Intelligence, 21, 2008, 1196–1203. [13] S. Jankowski, A. Oreziak, A. Skorupski, H. Kowalski, Z. Szymanski, and E. Piatkowska-Janko, Computer-aided morphological analysis of holter ECG recordings based on support vector learning system, Computers in Cardiology, 30, 2003, 597–600. [14] S. Jankowski and A. Oreziak, Learning system for computer-aided ECG analysis based on support vector machines, International Journal of Bioelectromagnetism, 5, 2003, 175–176. [15] S. Osowski, L.T. Hoai, and T. Markiewicz, Support vector machines based expert system for reliable heartbeat recognition, IEEE Transactions on Biomedical Engineering, 51, 2004, 582–589. [16] N. Acir, Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm, Neural Computer and Application, 14, 2005, 299–309. [17] E.D. Ubeyli, Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines, Computers in Biology and Medicine, 38, 2008, 14–22. [18] M.H. Song, J. Lee, S.P. Cho, K.J. Lee, and S.K. Yoo, Support vector machines based arrhythmia classification using reduced features, International Journal of Control Automatic and System, 3, 2005, 571–579. [19] N. Acir, A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems, Expert System with Application, 31, 2006, 150–158. [20] E.D. Ubeyli and I. Guler, Detection of electrocardiographic changes in partial epileptic patients using Lyapunov exponents with multilayer perceptron neural network, Engineering of Applications of Artificial Intelligence, 17(6), 2004, 567–576. [21] A. Subasi and M.I. Gursoy, EEG signal classification using PCA, ICA, LDA and support vector machines, Expert Systems with Applications, 37, 2010, 8659–8666. [22] A. Subasi, EEG signal classification using wavelet feature extraction and a mixture of expert model, Expert Systems with Applications, 32, 2007, 1084–1093. [23] M. Nielsen, E.N. Kamavuaka, M.M. Andersen, M.F. Lucas, and D. Farina, Optimal wavelets for biomedical signal compression, Medical and Biological Engineering Computing, 2006, 561–568. [24] A. Bousbia-Salah and M. Kedir-Talha, Compression of EEG signals based on biorthoghonal wavelet, Proc. 4th Int. Symp. IEEE on Applied Sciences in Biomedical and Communication Technologie, Bercelona, Espagna, 2011. [25] B. Boashash, M. Mesbah, and P. Colditz, Time frequency detection of EEG abnormalities, Chapter 15, Article 15, 5, 663–669. [26] S.Z. Mahmoodabadi and A. Ahmadian, ECG feature extraction using Daudechies wavelets, Proceeding Visualization, Imaging and Image Processing, 2005, 343–348. [27] X. Wang and K.K. Paliwal, Feature extraction and multidi-mensionality reduction algorithms and their applications in vowel recognition, Pattern Recognition, 36, 2003, 2429–2439. [28] A. Widodo and B. Yang, Application of nonlinear feature extraction and support vector machines for fault diagnostics for induction motors, Expert System with Applications, 33, 2007, 241–250. [29] M.C. Nait-Hamoud and A. Moussaoui, Two novel methods for multiclass ECG arrhythmias classification based on PCA, fuzzy SVM and unbalanced clustering, International Conference on Machine and Web Intelligence (ICMWI), 2010, 140–145. [30] V.N. Vapnik, The nature of statistical learning theory (New York, USA: Springer-Verlag, 1995). [31] S. Abe, Support vector machines for pattern classification: advances in computer vision and pattern recognition (London: Springer-Verlag, 2005). [32] Sh. Kumari and P. Jose, Seizure detection in EEG using time frequency analysis and SVM, Int. Conf. on Emerging Trends in Electrical and Computer Technology, 2011, 626–630. [33] M.D. Kedir-Talha and S.O. Slimane, Neural networks and SVM for heartbeat classification, Proc. 11th Int. Conf. IEEE on Information Sciences, Signal Processing and their Applications, Montreal, Canada, 2012, 853–857.
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