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Volumn 124, Issue , 2016, Pages 2-18

A novel genetic programming approach for epileptic seizure detection

Author keywords

Constructive crossover; Dynamic fitness value computation; Epilepsy; Genetic programming

Indexed keywords

ELECTROENCEPHALOGRAPHY; FEATURE EXTRACTION; GENETIC ALGORITHMS; GENETIC PROGRAMMING; HEALTH; NEUROLOGY; NEURONS; NEUROPHYSIOLOGY; SIGNAL DETECTION; SIGNAL PROCESSING; SLEEP RESEARCH;

EID: 84954475756     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2015.10.001     Document Type: Article
Times cited : (55)

References (44)
  • 3
    • 33846638294 scopus 로고    scopus 로고
    • Seizure prediction: the long and winding road
    • Mormann F., Andrzejak R.G., Elger C.E., Lehnertz K. Seizure prediction: the long and winding road. Brain 2007, 130(2):314-333.
    • (2007) Brain , vol.130 , Issue.2 , pp. 314-333
    • Mormann, F.1    Andrzejak, R.G.2    Elger, C.E.3    Lehnertz, K.4
  • 5
    • 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 Syst. Appl. 2007, 32(4):1084-1093.
    • (2007) Expert Syst. Appl. , vol.32 , Issue.4 , pp. 1084-1093
    • Subasi, A.1
  • 6
    • 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 R.G., Lehnertz K., Mormann F., Rieke C., David P., 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. Phys. Rev. E 2001, 64(6):061907.
    • (2001) Phys. Rev. E , vol.64 , Issue.6 , pp. 061907
    • Andrzejak, R.G.1    Lehnertz, K.2    Mormann, F.3    Rieke, C.4    David, P.5    Elger, C.E.6
  • 9
    • 84891056709 scopus 로고    scopus 로고
    • Semantic search-based genetic programming and the effect of intron deletion
    • Castelli M., Vanneschi L., Silva S. Semantic search-based genetic programming and the effect of intron deletion. IEEE Trans. Cybern. 2014, 44(1):103-113.
    • (2014) IEEE Trans. Cybern. , vol.44 , Issue.1 , pp. 103-113
    • Castelli, M.1    Vanneschi, L.2    Silva, S.3
  • 10
    • 84878230699 scopus 로고    scopus 로고
    • Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods
    • Rasekhi J., Mollaei M.R.K., Bandarabadi M., Teixeira C.A., Dourado A. Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods. J. Neurosci. Methods 2013, 217(1):9-16.
    • (2013) J. Neurosci. Methods , vol.217 , Issue.1 , pp. 9-16
    • Rasekhi, J.1    Mollaei, M.R.K.2    Bandarabadi, M.3    Teixeira, C.A.4    Dourado, A.5
  • 11
    • 84867919804 scopus 로고    scopus 로고
    • Seizure prediction using EEG spatiotemporal correlation structure
    • Williamson J.R., Bliss D.W., Browne D.W., Narayanan J.T. Seizure prediction using EEG spatiotemporal correlation structure. Epilepsy Behav. 2012, 25(2):230-238.
    • (2012) Epilepsy Behav. , vol.25 , Issue.2 , pp. 230-238
    • Williamson, J.R.1    Bliss, D.W.2    Browne, D.W.3    Narayanan, J.T.4
  • 15
    • 34247217946 scopus 로고    scopus 로고
    • Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform
    • Polat K., Güneş S. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl. Math. Comput. 2007, 187(2):1017-1026.
    • (2007) Appl. Math. Comput. , vol.187 , Issue.2 , pp. 1017-1026
    • Polat, K.1    Güneş, S.2
  • 16
    • 56349106179 scopus 로고    scopus 로고
    • Cross-correlation aided support vector machine classifier for classification of EEG signals
    • Chandaka S., Chatterjee A., Munshi S. Cross-correlation aided support vector machine classifier for classification of EEG signals. Expert Syst. Appl. 2009, 36(2):1329-1336.
    • (2009) Expert Syst. Appl. , vol.36 , Issue.2 , pp. 1329-1336
    • Chandaka, S.1    Chatterjee, A.2    Munshi, S.3
  • 17
    • 79959978998 scopus 로고    scopus 로고
    • Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection
    • Wang D., Miao D., Xie C. Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Syst. Appl. 2011, 38(11):14314-14320.
    • (2011) Expert Syst. Appl. , vol.38 , Issue.11 , pp. 14314-14320
    • Wang, D.1    Miao, D.2    Xie, C.3
  • 18
    • 84921806191 scopus 로고    scopus 로고
    • Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data
    • Smart O., Burrell L. Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data. Eng. Appl. Artif. Intell. 2015, 39:198-214.
    • (2015) Eng. Appl. Artif. Intell. , vol.39 , pp. 198-214
    • Smart, O.1    Burrell, L.2
  • 19
    • 81855221797 scopus 로고    scopus 로고
    • Detection of epileptic electroencephalogram based on permutation entropy and support vector machines
    • Nicolaou N., Georgiou J. Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst. Appl. 2012, 39(1):202-209.
    • (2012) Expert Syst. Appl. , vol.39 , Issue.1 , pp. 202-209
    • Nicolaou, N.1    Georgiou, J.2
  • 21
    • 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 Process. 2008, 88(7):1858-1867.
    • (2008) Signal Process. , vol.88 , Issue.7 , pp. 1858-1867
    • Ocak, H.1
  • 22
    • 77954612893 scopus 로고    scopus 로고
    • Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection
    • Liang S.-F., Wang H.-C., Chang W.-L. Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP J. Adv. Signal Process. 2010, 2010:62.
    • (2010) EURASIP J. Adv. Signal Process. , vol.2010 , pp. 62
    • Liang, S.-F.1    Wang, H.-C.2    Chang, W.-L.3
  • 23
    • 84865980798 scopus 로고    scopus 로고
    • Classification of seizure and nonseizure EEG signals using empirical mode decomposition
    • Bajaj V., Pachori R.B. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans. Inf. Technol. Biomed. 2012, 16(6):1135-1142.
    • (2012) IEEE Trans. Inf. Technol. Biomed. , vol.16 , Issue.6 , pp. 1135-1142
    • Bajaj, V.1    Pachori, R.B.2
  • 25
    • 79957981604 scopus 로고    scopus 로고
    • EEG signals classification using the k-means clustering and a multilayer perceptron neural network model
    • Orhan U., Hekim M., Ozer M. EEG signals classification using the k-means clustering and a multilayer perceptron neural network model. Expert Syst. Appl. 2011, 38(10):13475-13481.
    • (2011) Expert Syst. Appl. , vol.38 , Issue.10 , pp. 13475-13481
    • Orhan, U.1    Hekim, M.2    Ozer, M.3
  • 26
    • 26944458497 scopus 로고    scopus 로고
    • Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients
    • Güler I., Übeyli E.D. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J. Neurosci. Methods 2005, 148(2):113-121.
    • (2005) J. Neurosci. Methods , vol.148 , Issue.2 , pp. 113-121
    • Güler, I.1    Übeyli, E.D.2
  • 27
    • 51349117677 scopus 로고    scopus 로고
    • A radial basis function neural network model for classification of epilepsy using EEG signals
    • Aslan K., Bozdemir H., Şahin C., Oğulata S.N., Erol R. A radial basis function neural network model for classification of epilepsy using EEG signals. J. Med. Syst. 2008, 32(5):403-408.
    • (2008) J. Med. Syst. , vol.32 , Issue.5 , pp. 403-408
    • Aslan, K.1    Bozdemir, H.2    Şahin, C.3    Oğulata, S.N.4    Erol, R.5
  • 28
    • 24144470790 scopus 로고    scopus 로고
    • Recurrent neural networks employing Lyapunov exponents for EEG signals classification
    • Güler N.F., Übeyli E.D., Güler I. Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst. Appl. 2005, 29(3):506-514.
    • (2005) Expert Syst. Appl. , vol.29 , Issue.3 , pp. 506-514
    • Güler, N.F.1    Übeyli, E.D.2    Güler, I.3
  • 29
    • 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 J.R., Pazos A. Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J. Neurosci. methods 2010, 191(1):101-109.
    • (2010) J. Neurosci. methods , vol.191 , Issue.1 , pp. 101-109
    • Guo, L.1    Rivero, D.2    Dorado, J.3    Rabunal, J.R.4    Pazos, A.5
  • 30
    • 77957685691 scopus 로고    scopus 로고
    • Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks
    • Guo L., Rivero D., Pazos A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J. Neurosci. Methods 2010, 193(1):156-163.
    • (2010) J. Neurosci. Methods , vol.193 , Issue.1 , pp. 156-163
    • Guo, L.1    Rivero, D.2    Pazos, A.3
  • 33
    • 35448991660 scopus 로고    scopus 로고
    • On the trend, detrending, and variability of nonlinear and nonstationary time series
    • Wu Z., Huang N.E., Long S.R., Peng C.-K. On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc. Natl. Acad. Sci. 2007, 104(38):14889-14894.
    • (2007) Proc. Natl. Acad. Sci. , vol.104 , Issue.38 , pp. 14889-14894
    • Wu, Z.1    Huang, N.E.2    Long, S.R.3    Peng, C.-K.4
  • 34
    • 78649417989 scopus 로고    scopus 로고
    • EMD: A package for empirical mode decomposition and Hilbert spectrum
    • Kim D., Oh H.-S. EMD: A package for empirical mode decomposition and Hilbert spectrum. R J. 2009, 1(1):40-46.
    • (2009) R J. , vol.1 , Issue.1 , pp. 40-46
    • Kim, D.1    Oh, H.-S.2
  • 36
    • 35148862496 scopus 로고    scopus 로고
    • A new crossover operator in genetic programming for object classification
    • Zhang M., Gao X., Lou W. A new crossover operator in genetic programming for object classification. IEEE Trans. Syst. Man Cybern. B: Cybern. 2007, 37(5):1332-1343.
    • (2007) IEEE Trans. Syst. Man Cybern. B: Cybern. , vol.37 , Issue.5 , pp. 1332-1343
    • Zhang, M.1    Gao, X.2    Lou, W.3
  • 39
    • 71349088028 scopus 로고    scopus 로고
    • Entropies based detection of epileptic seizures with artificial neural network classifiers
    • Pravin Kumar S., Sriraam N., Benakop P., Jinaga B. Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Syst. Appl. 2010, 37(4):3284-3291.
    • (2010) Expert Syst. Appl. , vol.37 , Issue.4 , pp. 3284-3291
    • Pravin Kumar, S.1    Sriraam, N.2    Benakop, P.3    Jinaga, B.4
  • 40
    • 56349101801 scopus 로고    scopus 로고
    • Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy
    • Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 2009, 36(2):2027-2036.
    • (2009) Expert Syst. Appl. , vol.36 , Issue.2 , pp. 2027-2036
    • Ocak, H.1
  • 42
    • 56549122554 scopus 로고    scopus 로고
    • Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks
    • Übeyli E.D. Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks. Digital Signal Process. 2009, 19(1):134-143.
    • (2009) Digital Signal Process. , vol.19 , Issue.1 , pp. 134-143
    • Übeyli, E.D.1
  • 43
    • 71749109171 scopus 로고    scopus 로고
    • Lyapunov exponents/probabilistic neural networks for analysis of EEG signals
    • Übeyli E.D. Lyapunov exponents/probabilistic neural networks for analysis of EEG signals. Expert Syst. Appl. 2010, 37(2):985-992.
    • (2010) Expert Syst. Appl. , vol.37 , Issue.2 , pp. 985-992
    • Übeyli, E.D.1
  • 44
    • 77951208271 scopus 로고    scopus 로고
    • Epileptic EEG detection using the linear prediction error energy
    • Altunay S., Telatar Z., Erogul O. Epileptic EEG detection using the linear prediction error energy. Expert Syst. Appl. 2010, 37(8):5661-5665.
    • (2010) Expert Syst. Appl. , vol.37 , Issue.8 , pp. 5661-5665
    • Altunay, S.1    Telatar, Z.2    Erogul, O.3


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