메뉴 건너뛰기




Volumn 133, Issue , 2014, Pages 271-279

Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine

Author keywords

Discrete wavelet transform (DWT); Electroencephalogram (EEG); Fuzzy approximate entropy (fApEn); Support vector machines (SVMs)

Indexed keywords

DISCRETE WAVELET TRANSFORMS; ELECTROENCEPHALOGRAPHY; ENTROPY; NEUROLOGY; RADIAL BASIS FUNCTION NETWORKS; WAVELET ANALYSIS;

EID: 84894582585     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.11.009     Document Type: Article
Times cited : (295)

References (42)
  • 1
    • 84894513789 scopus 로고    scopus 로고
    • WHO. Media Centre, Epilepsy, Fact Sheet. (accessed 2013).
    • WHO. Media Centre, Epilepsy, Fact Sheet. (accessed 2013). http://www.who.int/mediacentre/factsheets/fs999/en/.
  • 2
    • 84894549148 scopus 로고    scopus 로고
    • NINDS. Seizure and Epilepsy: Hope Through Research. National Institute of Neurological Disorders Available from: (accessed 2013).
    • NINDS. Seizure and Epilepsy: Hope Through Research. National Institute of Neurological Disorders Available from: (accessed 2013). http://www.ninds.nih.gov/disorders/epilepsy/detail_epilepsy.htm.
  • 3
    • 34547573516 scopus 로고    scopus 로고
    • Mixed band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection
    • Dastidar S.G., Adeli H., Dadmehr N. Mixed band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans. Biomed. Eng. 2007, 54(9):1545-1551.
    • (2007) IEEE Trans. Biomed. Eng. , vol.54 , Issue.9 , pp. 1545-1551
    • Dastidar, S.G.1    Adeli, H.2    Dadmehr, N.3
  • 4
    • 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. J. Neurosci. Method 2003, 123(1):69-87.
    • (2003) J. Neurosci. Method , vol.123 , Issue.1 , pp. 69-87
    • Adeli, H.1    Zhou, Z.2    Dadmehr, N.3
  • 6
    • 0037410182 scopus 로고    scopus 로고
    • Wavelet analysis of generalized tonic-clonic epileptic seizures
    • Rosso O.A., Blanco S., Rabinowicz A. Wavelet analysis of generalized tonic-clonic epileptic seizures. Signal Process. 2003, 83:1275-1289.
    • (2003) Signal Process. , vol.83 , pp. 1275-1289
    • Rosso, O.A.1    Blanco, S.2    Rabinowicz, A.3
  • 7
    • 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
    • (1-8)
    • Andrzejak R.G., Lehnertz K., Rieke C 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. (1-8).
    • (2001) Phys. Rev. E , vol.64 , Issue.6 , pp. 061907
    • Andrzejak, R.G.1    Lehnertz, K.2    Rieke, C.3
  • 8
    • 33846672121 scopus 로고    scopus 로고
    • A wavelet-chaos methodology for analysis of EEGs and EEG sub-bands to detect seizure and epilepsy
    • Adeli H., Dastidar S.G., Dadmehr N. A wavelet-chaos methodology for analysis of EEGs and EEG sub-bands to detect seizure and epilepsy. IEEE Trans. Biomed. Eng. 2007, 54(2):205-211.
    • (2007) IEEE Trans. Biomed. Eng. , vol.54 , Issue.2 , pp. 205-211
    • Adeli, H.1    Dastidar, S.G.2    Dadmehr, N.3
  • 9
    • 77957252563 scopus 로고    scopus 로고
    • Detection of seizures in EEG using sub-band nonlinear parameters and genetic algorithm
    • Hsu K.C., Yu S.N. Detection of seizures in EEG using sub-band nonlinear parameters and genetic algorithm. Comput. Biol. Med. 2010, 40:823-830.
    • (2010) Comput. Biol. Med. , vol.40 , pp. 823-830
    • Hsu, K.C.1    Yu, S.N.2
  • 10
    • 0026015905 scopus 로고
    • Approximate entropy as a measure of system complexity
    • Pincus S.M. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA 1991, 88:2297-2301.
    • (1991) Proc. Natl. Acad. Sci. USA , vol.88 , pp. 2297-2301
    • Pincus, S.M.1
  • 11
    • 0032077575 scopus 로고    scopus 로고
    • Estimating regularity in epileptic seizure time-series data: a complexity-measure approach
    • Radhakrishnan N., Gangadhar B. Estimating regularity in epileptic seizure time-series data: a complexity-measure approach. IEEE Eng. Med. Biol. 1998, 17(3):89-94.
    • (1998) IEEE Eng. Med. Biol. , vol.17 , Issue.3 , pp. 89-94
    • Radhakrishnan, N.1    Gangadhar, B.2
  • 12
    • 0001081093 scopus 로고    scopus 로고
    • Epileptic activity recognition in EEG recording
    • Diambra L., Figueiredo J., Malta C. Epileptic activity recognition in EEG recording. Phys. A: Stat. Mech. Appl. 1999, 273(3 and 4):495-505.
    • (1999) Phys. A: Stat. Mech. Appl. , vol.273 , Issue.3-4 , pp. 495-505
    • Diambra, L.1    Figueiredo, J.2    Malta, C.3
  • 13
    • 57849119711 scopus 로고    scopus 로고
    • Measuring complexity using FuzzyEn, ApEn and SampEN
    • Chen W., Zhuang J., Yu W., Wang Z. Measuring complexity using FuzzyEn, ApEn and SampEN. Med. Eng. Phys. 2009, 31:61-68.
    • (2009) Med. Eng. Phys. , vol.31 , pp. 61-68
    • Chen, W.1    Zhuang, J.2    Yu, W.3    Wang, Z.4
  • 14
    • 78650699439 scopus 로고    scopus 로고
    • Classification of ventricular tachycardia and fibrillation using fuzzy similarity based approximate entropy
    • Xie H.B, Gao Z.M., Liu H. Classification of ventricular tachycardia and fibrillation using fuzzy similarity based approximate entropy. Expert Syst. Appl. 2011, 38:3973-3981.
    • (2011) Expert Syst. Appl. , vol.38 , pp. 3973-3981
    • Xie, H.B.1    Gao, Z.M.2    Liu, H.3
  • 15
    • 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(5):2027-2036.
    • (2009) Expert Syst. Appl. , vol.36 , Issue.5 , pp. 2027-2036
    • Ocak, H.1
  • 16
    • 77957685691 scopus 로고    scopus 로고
    • Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks
    • Guo L., Riveer D., Pazaos A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J. Neurosci. Methods 2010, 193:156-163.
    • (2010) J. Neurosci. Methods , vol.193 , pp. 156-163
    • Guo, L.1    Riveer, D.2    Pazaos, A.3
  • 17
    • 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. J. Med. Syst. 2005, 29(6):647-660.
    • (2005) J. Med. Syst. , vol.29 , Issue.6 , pp. 647-660
    • Srinivasan, V.1    Eswaran, C.2    Sriraam, N.3
  • 18
    • 34248567678 scopus 로고    scopus 로고
    • Approximate entropy based epileptic EEG detection using artificial neural networks
    • Srinivasan V., Eswaran C., Sriraam N. Approximate entropy based epileptic EEG detection using artificial neural networks. IEEE Trans. Inf. Technol. Biomed. 2007, 11(3):288-295.
    • (2007) IEEE Trans. Inf. Technol. Biomed. , vol.11 , Issue.3 , pp. 288-295
    • Srinivasan, V.1    Eswaran, C.2    Sriraam, N.3
  • 19
    • 80052836687 scopus 로고    scopus 로고
    • Epileptic EEG classification based on extreme learning machine and nonlinear features
    • Yuan Q., Zhou W., Li S., Cai D. Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res. 2011, 96:29-38.
    • (2011) Epilepsy Res. , vol.96 , pp. 29-38
    • Yuan, Q.1    Zhou, W.2    Li, S.3    Cai, D.4
  • 20
    • 81855221797 scopus 로고    scopus 로고
    • Detection of epileptic electroencephalogram based on permutation entropy and support vector machine
    • Nicolaou N., Georgiou J. Detection of epileptic electroencephalogram based on permutation entropy and support vector machine. Expert Syst. Appl. 2012, 39:202-209.
    • (2012) Expert Syst. Appl. , vol.39 , pp. 202-209
    • Nicolaou, N.1    Georgiou, J.2
  • 22
    • 70349472753 scopus 로고    scopus 로고
    • Least square support vector machine employing model-based methods coefficients for analysis of EEG signals
    • Ubeyli E.D. Least square support vector machine employing model-based methods coefficients for analysis of EEG signals. Expert Syst. Appl. 2010, 37:233-239.
    • (2010) Expert Syst. Appl. , vol.37 , pp. 233-239
    • Ubeyli, E.D.1
  • 23
    • 79953729618 scopus 로고    scopus 로고
    • Classification of electroencephalogram signals with combined time and frequency features
    • Iscan Z., Dokur Z., Demiralap T. Classification of electroencephalogram signals with combined time and frequency features. Expert Syst. Appl. 2011, 38:10499-10505.
    • (2011) Expert Syst. Appl. , vol.38 , pp. 10499-10505
    • Iscan, Z.1    Dokur, Z.2    Demiralap, T.3
  • 24
    • 0024700097 scopus 로고
    • A theory for multi-resolution signal decomposition: the wavelet representation
    • Mallat S. A theory for multi-resolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 1989, 11(7):674-693.
    • (1989) IEEE Trans. Pattern Anal. Mach. Intell. , vol.11 , Issue.7 , pp. 674-693
    • Mallat, S.1
  • 28
    • 80052931335 scopus 로고    scopus 로고
    • A comparative study of wavelet families for EEG signals classification
    • Gandhi T., Panigrahi B.K., Anand S. A comparative study of wavelet families for EEG signals classification. Neurocomputing 2011, 74:3051-3057.
    • (2011) Neurocomputing , vol.74 , pp. 3051-3057
    • Gandhi, T.1    Panigrahi, B.K.2    Anand, S.3
  • 29
    • 84877779740 scopus 로고    scopus 로고
    • SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors
    • Virmani J., Kumar V., Kalra.N., Khandelwal N. SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors. J. Digital Imaging 2013, 26(3):530-543.
    • (2013) J. Digital Imaging , vol.26 , Issue.3 , pp. 530-543
    • Virmani, J.1    Kumar, V.2    Kalra, N.3    Khandelwal, N.4
  • 30
    • 0037076322 scopus 로고    scopus 로고
    • Selection bias in gene extraction on the basis of microarray gene expression data
    • C C.Ambroise, McLachlan G.J. Selection bias in gene extraction on the basis of microarray gene expression data. Proc. Natl. Acad. USA 2002, 99(10):6562-6566.
    • (2002) Proc. Natl. Acad. USA , vol.99 , Issue.10 , pp. 6562-6566
    • Ambroise, C.C.1    McLachlan, G.J.2
  • 31
    • 22144480299 scopus 로고    scopus 로고
    • Epileptic seizure detection using dynamic wavelet network
    • Subasi A. Epileptic seizure detection using dynamic wavelet network. Expert Syst. Appl. 2005, 29(2):343-355.
    • (2005) Expert Syst. Appl. , vol.29 , Issue.2 , pp. 343-355
    • Subasi, A.1
  • 32
    • 0842310823 scopus 로고    scopus 로고
    • A neural-network-based detection of epilepsy
    • Nigam V., Graupe D. A neural-network-based detection of epilepsy. Neurol. Res. 2004, 26(1):55-60.
    • (2004) Neurol. Res. , vol.26 , Issue.1 , pp. 55-60
    • Nigam, V.1    Graupe, D.2
  • 34
    • 34247217946 scopus 로고    scopus 로고
    • Classification of epileptic form EEG using a hybrid system based on decision tree classifier and fast Fourier transform
    • Polat K., Günes S. Classification of epileptic form 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    Günes, S.2
  • 35
    • 38749083808 scopus 로고    scopus 로고
    • Automatic seizure detection based on time-frequency analysis and artificial neural networks
    • (Article ID 80510)
    • Tzallas A., Tsipouras M., Fotiadis D. Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput. Intell. Neurosci. 2007, 13. (Article ID 80510).
    • (2007) Comput. Intell. Neurosci. , vol.13
    • Tzallas, A.1    Tsipouras, M.2    Fotiadis, D.3
  • 36
    • 67650751415 scopus 로고    scopus 로고
    • Classification of EEG signals using relative wavelet energy and artificial neural networks. In: Proceedings of the First ACM/SIGEVO, Summit on Genetic and Evolutionary Computation (GEC'09), Shanghai, China, 12-14 June
    • L. Guo, D. Rivero, J. Seoane, A. Pazos, Classification of EEG signals using relative wavelet energy and artificial neural networks. In: Proceedings of the First ACM/SIGEVO, Summit on Genetic and Evolutionary Computation (GEC'09), Shanghai, China, 12-14 June 2009, pp. 177-184.
    • (2009) , pp. 177-184
    • Guo, L.1    Rivero, D.2    Seoane, J.3    Pazos, A.4
  • 37
    • 77957830692 scopus 로고    scopus 로고
    • EEG signal classification using PCA, ICA, LDA and support vector machine
    • Subasi A., Gursoy M.I. EEG signal classification using PCA, ICA, LDA and support vector machine. Expert Syst. Appl. 2010, 37:8659-8666.
    • (2010) Expert Syst. Appl. , vol.37 , pp. 8659-8666
    • Subasi, A.1    Gursoy, M.I.2
  • 38
    • 77955054723 scopus 로고    scopus 로고
    • Automatic epileptic seizure detection in EEG based on line length feature and artificial neural network
    • Guo L., Rivero D., Dorado J., Rabunal J.R., Pazos A. Automatic epileptic seizure detection in EEG based on line length feature and artificial neural network. J. Neurosci. Methods 2010, 19:1101-1109.
    • (2010) J. Neurosci. Methods , vol.19 , pp. 1101-1109
    • Guo, L.1    Rivero, D.2    Dorado, J.3    Rabunal, J.R.4    Pazos, A.5
  • 39
    • 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, 38:13475-13481.
    • (2011) Expert Syst. Appl. , vol.38 , pp. 13475-13481
    • Orhan, U.1    Hekim, M.2    Ozer, M.3
  • 40
    • 79953693243 scopus 로고    scopus 로고
    • Automatic feature extraction using genetic programming: an application to epileptic EEG classification
    • L. Guo, D. Rivero, J. Dorado, C. R. Munteanu, A. Pazos, Automatic feature extraction using genetic programming: an application to epileptic EEG classification. Expert Syst. Appl. 38 (2011) 10425-10436.
    • (2011) Expert Syst. Appl. , vol.38 , pp. 10425-10436
    • Guo, L.1    Rivero, D.2    Dorado, J.3    Munteanu, C.R.4    Pazos, A.5
  • 41
    • 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, 38:14314-14320.
    • (2011) Expert Syst. Appl. , vol.38 , pp. 14314-14320
    • Wang, D.1    Miao, D.2    Xie, C.3
  • 42
    • 84894569467 scopus 로고    scopus 로고
    • EEG Time Series Data (Department of Epileptology University of Bonn, Germany)
    • EEG Time Series Data (Department of Epileptology University of Bonn, Germany) http://epileptologie-bonn.de/cms/front_content.php?idcat=193&lang=3&changelang=3.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.