-
1
-
-
84894513789
-
-
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
-
-
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
-
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
-
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
-
5
-
-
0031239693
-
Automatic seizure detection in the newborn: methods and initial evaluation
-
Gotman J., Flanagah D., Zhang J., Rosenblatt B. Automatic seizure detection in the newborn: methods and initial evaluation. Electroencephalogr. Clin. Neurophysiol. 1997, 103:356-362.
-
(1997)
Electroencephalogr. Clin. Neurophysiol.
, vol.103
, pp. 356-362
-
-
Gotman, J.1
Flanagah, D.2
Zhang, J.3
Rosenblatt, B.4
-
6
-
-
0037410182
-
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
-
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
-
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
-
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
-
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
-
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
-
13
-
-
57849119711
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
21
-
-
84859212205
-
Automatic diagnosis of epileptic EEG using entropies
-
Acharya U.R., Molinari F., Sree S.V., Chattopadhyay S. Automatic diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control 2012, 7(4):401-408.
-
(2012)
Biomed. Signal Process. Control
, vol.7
, Issue.4
, pp. 401-408
-
-
Acharya, U.R.1
Molinari, F.2
Sree, S.V.3
Chattopadhyay, S.4
-
22
-
-
70349472753
-
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
-
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
-
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
-
27
-
-
78751613317
-
EEG-based neonatal seizure detection with support vector machines
-
Temko A., Thomas E., Marnane W., Lightbody G., Boylan G. EEG-based neonatal seizure detection with support vector machines. Clin. Neurophysiol. 2011, 122:464-473.
-
(2011)
Clin. Neurophysiol.
, vol.122
, pp. 464-473
-
-
Temko, A.1
Thomas, E.2
Marnane, W.3
Lightbody, G.4
Boylan, G.5
-
28
-
-
80052931335
-
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
-
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
-
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
-
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
-
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
-
33
-
-
27744537035
-
Entropies for detection of epilepsy in EEG
-
Kannathal N., Choo M., Acharya U., Sadasivan P. Entropies for detection of epilepsy in EEG. Comput. Methods Prog. Biomed. 2005, 80(3):187-194.
-
(2005)
Comput. Methods Prog. Biomed.
, vol.80
, Issue.3
, pp. 187-194
-
-
Kannathal, N.1
Choo, M.2
Acharya, U.3
Sadasivan, P.4
-
34
-
-
34247217946
-
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
-
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
-
-
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
-
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
-
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
-
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
-
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
-
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
-
-
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.
-
-
-
|