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Volumn 23, Issue , 2013, Pages 84-95

Epileptic seizure identification from electroencephalography signal using DE-RBFNs ensemble

Author keywords

Bagging; Classification; Differential evolution; EEG; Radial basis function neural networks

Indexed keywords


EID: 84896964008     PISSN: 18770509     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2013.10.012     Document Type: Conference Paper
Times cited : (22)

References (40)
  • 6
    • 61349154936 scopus 로고    scopus 로고
    • Neural network-based computer aided diagnosis in classification of primary generalized epilepsy by EEG signals
    • Ogulata SN, Sahin C, Erol R. Neural network-based computer aided diagnosis in classification of primary generalized epilepsy by EEG signals. Journal of Medical Systems, 2009; 33:107-112.
    • (2009) Journal of Medical Systems , vol.33 , pp. 107-112
    • Ogulata, S.N.1    Sahin, C.2    Erol, R.3
  • 7
    • 0037441741 scopus 로고    scopus 로고
    • Analysis of EEG records in an epileptic patient using wavelet transform
    • Adeli H, Zhou Z, Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods, 2003; 123:69-87.
    • (2003) Journal of Neuroscience Methods , vol.123 , pp. 69-87
    • Adeli, H.1    Zhou, Z.2    Dadmehr, N.3
  • 8
    • 17844371713 scopus 로고    scopus 로고
    • Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
    • Subasi A. Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients. Expert Systems with Applications, 2005; 28:701-711.
    • (2005) Expert Systems with Applications , vol.28 , pp. 701-711
    • Subasi, A.1
  • 9
    • 33751396389 scopus 로고    scopus 로고
    • EEG signal classification using wavelet feature extraction and a mixture of expert model
    • Subasi A. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications, 2007; 32:1084-1093.
    • (2007) Expert Systems with Applications , vol.32 , pp. 1084-1093
    • Subasi, A.1
  • 10
    • 17744374301 scopus 로고    scopus 로고
    • Classification of EEG signals using neural network and logistic regression
    • Subasi A, Ercelebi E. Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine, 2005; 78:87-99.
    • (2005) Computer Methods and Programs in Biomedicine , vol.78 , pp. 87-99
    • Subasi, A.1    Ercelebi, E.2
  • 11
    • 0031141135 scopus 로고    scopus 로고
    • Classification of EEG signals using the wavelet transform
    • Hazarika N, Chen JZ, Tsoi A.C., Sergejew A. Classification of EEG signals using the wavelet transform. Signal Processing, 1997; 59(1): 61-72.
    • (1997) Signal Processing , vol.59 , Issue.1 , pp. 61-72
    • Hazarika, N.1    Chen, J.Z.2    Tsoi, A.C.3    Sergejew, A.4
  • 12
    • 58549111381 scopus 로고    scopus 로고
    • Combined neural network model employing wavelet coefficients for EEG signals classification
    • Ubeyli, ED. Combined neural network model employing wavelet coefficients for EEG signals classification. Digital Signal Processing, 2009; 19: 297-308.
    • (2009) Digital Signal Processing , vol.19 , pp. 297-308
    • Ubeyli, E.D.1
  • 13
    • 56349123198 scopus 로고    scopus 로고
    • Decision support systems for time-varying biomedical signals: Eeg signals classification
    • Ubeyli ED. Decision support systems for time-varying biomedical signals: EEG signals classification. Expert Systems with Applications, 2009; 36: 2275-2284.
    • (2009) Expert Systems with Applications , vol.36 , pp. 2275-2284
    • Ubeyli, E.D.1
  • 14
    • 26944479675 scopus 로고    scopus 로고
    • Automatic seizure detection in EEG using logistic regression and artificial neural network
    • Alkan A, Koklukaya E, Subasi A. Automatic seizure detection in EEG using logistic regression and artificial neural network. Journal of Neuroscience Methods, 2005; 148:167-176.
    • (2005) Journal of Neuroscience Methods , vol.148 , pp. 167-176
    • Alkan, A.1    Koklukaya, E.2    Subasi, A.3
  • 15
    • 51349117677 scopus 로고    scopus 로고
    • A radial basis function neural network model for classification of epilepsy using EEG signals
    • Aslan K, Bozdemir H, Sahin S., Ogulata SN, Erol R. A radial basis function neural network model for classification of epilepsy using EEG signals. Journal of Medical Systems, 2008; 32:403408.
    • (2008) Journal of Medical Systems , vol.32 , pp. 403408
    • Aslan, K.1    Bozdemir, H.2    Sahin, S.3    Ogulata, S.N.4    Erol, R.5
  • 16
    • 0033990625 scopus 로고    scopus 로고
    • Recurrent neural network based prediction of epileptic seizures in intra and extracranial EEG
    • Petrosian A Prokhorov D, Homan R, Dashei R, Wunsch D. Recurrent neural network based prediction of epileptic seizures in intra and extracranial EEG. Neurocomputing, 2000; 30:201-218.
    • (2000) Neurocomputing , vol.30 , pp. 201-218
    • Petrosian, A.1    Prokhorov, D.2    Homan, R.3    Dashei, R.4    Wunsch, D.5
  • 17
    • 0030219951 scopus 로고    scopus 로고
    • Detection of seizure activity in EEG by an artificial neural network: A preliminary study
    • Pradhan N, Sadasivan PK, Arunodaya GR Detection of seizure activity in EEG by an artificial neural network: A preliminary study. Computers and Biomedical Research, 1996; 29:303-313.
    • (1996) Computers and Biomedical Research , vol.29 , pp. 303-313
    • Pradhan, N.1    Sadasivan, P.K.2    Arunodaya, G.R.3
  • 18
    • 24044474732 scopus 로고    scopus 로고
    • Artificial neural network based epileptic detection using time-domain and frequency-domain features
    • Srinivasan V, Eswaran C, Sriraam N. Artificial neural network based epileptic detection using time-domain and frequency-domain features. Journal of Medical Systems, 2005; 29(6):647-660.
    • (2005) Journal of Medical Systems , vol.29 , Issue.6 , pp. 647-660
    • Srinivasan, V.1    Eswaran, C.2    Sriraam, N.3
  • 19
    • 22144480299 scopus 로고    scopus 로고
    • Epileptic seizure detection using dynamic wavelet network
    • Subasi A. Epileptic seizure detection using dynamic wavelet network. Expert Systems with Applications, 2005; 29:343-355.
    • (2005) Expert Systems with Applications , vol.29 , pp. 343-355
    • Subasi, A.1
  • 20
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman L. Bagging Predictors. Machine Learning, 1996; 24:123-140.
    • (1996) Machine Learning , vol.24 , pp. 123-140
    • Breiman, L.1
  • 22
    • 26444554549 scopus 로고    scopus 로고
    • Mining the customer credit using classification and regression tree and multivariate adaptive regression splines
    • Lee TS, Chiu CC, Chou Y.C., Lu CJ Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics and Data Analysis, 2006; 50:1113-1130.
    • (2006) Computational Statistics and Data Analysis , vol.50 , pp. 1113-1130
    • Lee, T.S.1    Chiu, C.C.2    Chou, Y.C.3    Lu, C.J.4
  • 23
    • 0034250160 scopus 로고    scopus 로고
    • Experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • Dietterich, TG Experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Machine Learning, 2000; 40:139-157.
    • (2000) Machine Learning , vol.40 , pp. 139-157
    • Dietterich, T.G.1
  • 25
    • 41249099701 scopus 로고    scopus 로고
    • Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm
    • Ocak H. Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Signal Processing, 2008; 88: 1858-1867.
    • (2008) Signal Processing , vol.88 , pp. 1858-1867
    • Ocak, H.1
  • 27
    • 0000621802 scopus 로고
    • Multivariable functional interpolation and adaptive networks
    • Broomhead, DS, Lowe D. Multivariable functional interpolation and adaptive networks. Complex systems, 1988; 2:321-355.
    • (1988) Complex Systems , vol.2 , pp. 321-355
    • Broomhead, D.S.1    Lowe, D.2
  • 28
    • 0000672424 scopus 로고
    • Fast learning networks of locally-tuned processing units
    • Moody J, Darken CJ Fast learning networks of locally-tuned processing units. Neural Computation, 1989; 6(4):281-294.
    • (1989) Neural Computation , vol.6 , Issue.4 , pp. 281-294
    • Moody, J.1    Darken, C.J.2
  • 29
    • 0000106040 scopus 로고
    • Universal approximation using radial basis function networks
    • Park J, Sandberg JW Universal approximation using radial basis function networks. Neural Computation, 1991; 3:246-257.
    • (1991) Neural Computation , vol.3 , pp. 246-257
    • Park, J.1    Sandberg, J.W.2
  • 30
    • 0025839504 scopus 로고
    • A Gaussian potential function network with hierarchically self organizing learning
    • Lee S, Kil RM A Gaussian potential function network with hierarchically self organizing learning. Neural Networks, 1991; 4:207-224.
    • (1991) Neural Networks , vol.4 , pp. 207-224
    • Lee, S.1    Kil, R.M.2
  • 31
    • 0033732354 scopus 로고    scopus 로고
    • Selecting radial basis function network centers with recursive orthogonal least squares training
    • Gomm JB, Yu DL Selecting radial basis function network centers with recursive orthogonal least squares training. IEEE Transactions of Neural Networks, 2000; 11(3):306-314.
    • (2000) IEEE Transactions of Neural Networks , vol.11 , Issue.3 , pp. 306-314
    • Gomm, J.B.1    Yu, D.L.2
  • 32
    • 84887023292 scopus 로고    scopus 로고
    • Differential evolution based optimization of kernel parameters in radial basis function networks for classification
    • Dash CSK, Behera A Dehuri S, Cho SB. Differential evolution based optimization of kernel parameters in radial basis function networks for classification. International Journal of Applied Evolutionary Computation, 2012; 4(1):56-80.
    • (2012) International Journal of Applied Evolutionary Computation , vol.4 , Issue.1 , pp. 56-80
    • Dash, C.S.K.1    Behera, A.2    Dehuri, S.3    Cho, S.B.4
  • 33
    • 0033115654 scopus 로고    scopus 로고
    • System design by constraint adaption and differential evolution
    • Stron R. System design by constraint adaption and differential evolution. IEEE Transactions on Evolutionary Computation, 1999; 3: 22-34.
    • (1999) IEEE Transactions on Evolutionary Computation , vol.3 , pp. 22-34
    • Stron, R.1
  • 36
    • 0348151971 scopus 로고    scopus 로고
    • Combining classifiers: Soft computing solutions
    • S. K. Pal and A. Ghosh (Eds.) World Scientific
    • Kuncheva, LI. Combining classifiers: soft computing solutions. In S. K. Pal and A. Ghosh (Eds.), Pattern Recognition from Classical to Modern Approaches, World Scientific, 2003, p. 427-449.
    • (2003) Pattern Recognition from Classical to Modern Approaches , pp. 427-449
    • Kuncheva, L.I.1
  • 37
    • 0035682573 scopus 로고    scopus 로고
    • Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state
    • Andrzejak RG, Lehnertz K, Mormann F., Rieke C, David P, Elger CE 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, 2001; 64(6): 1-8.
    • (2001) Physical Review E , vol.64 , Issue.6 , pp. 1-8
    • Andrzejak, R.G.1    Lehnertz, K.2    Mormann, F.3    Rieke, C.4    David, P.5    Elger, C.E.6
  • 38
    • 84896955720 scopus 로고    scopus 로고
    • EEG time series are available under
    • EEG time series are available under (http://www.meb.uni-bonn.de/ epileptologie/science/physik/eegdata.html).
  • 39
    • 77955054723 scopus 로고    scopus 로고
    • Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks
    • Guo L, Rivero D, Dorado J., Rabunal JR, Pazos A. Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. Journal of Neuroscience Methods, 2010; 191:101-109.
    • (2010) Journal of Neuroscience Methods , vol.191 , pp. 101-109
    • Guo, L.1    Rivero, D.2    Dorado, J.3    Rabunal, J.R.4    Pazos, A.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.