Wavelet-based feature extraction for classification of epileptic seizure EEG signal

References

• Kalayci T, Ozdamar O. Wavelet preprocessing for automated neural network detection of EEG spikes. IEEE Eng Med Biol Mag. 1995;14:160–166.
   Google Scholar
• Nigam V, Graupe D. A neural-network-based detection of epilepsy. Neurol Res. 2004;26:55–60.
   PubMed Web of Science ®Google Scholar
• Jahankhani P, Kodogiannis V, Revett K. EEG signal classification using wavelet feature extraction and neural networks. In: IEEE John Vincent Atanasoff 2006 international symposium on modern computing (JVA’06); 2006. p. 52–57.
   Google Scholar
• Subasi A. Epileptic seizure detection using dynamic wavelet network. Expert Syst Appl. 2005;29:343–355.
   Web of Science ®Google Scholar
• Subasi A. Automatic detection of epileptic seizure using dynamic fuzzy neural networks. Expert Syst Appl. 2006;31:320–328.
   Web of Science ®Google Scholar
• Subasi A. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl. 2007;32:1084–1093.
   Web of Science ®Google Scholar
• Ubeyli E. Combined neural network model employing wavelet coefficients for EEG signals classification. Digital Signal Process. 2009;19:297–308.
   Web of Science ®Google Scholar
• Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transforms and approximate entropy. Expert Syst Appl. 2009;36:2027–2036.
   Web of Science ®Google Scholar
• Guo L, Rivero D, Pazos A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods. 2010;193:156–163.
   PubMed Web of Science ®Google Scholar
• Orhan U, Hekim M, Ozer M. EEG signals classification using k-means clustering and a multilayer perceptron neural network model. Expert Syst Appl. 2011;38:13475–13481.
   Web of Science ®Google Scholar
• Iscan Z, Dokur Z, T, D. Classification of electroencephalogram signals with combined time and frequency features. Expert Syst Appl. 2011;38:10499–10505.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Xie S, Krishnan S. Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis. Med Biol Eng Comput. 2013;51:49–60.
   PubMed Web of Science ®Google Scholar
• Janjarasjitt S. Classification of the epileptic EEGs using the wavelet based scale variance feature. Int J Appl Biomed Eng. 2010;3:19–25.
   Google Scholar
• Kumar Y, Dewal ML, Anand RS. Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. SIViP. 2014;8:1323–1334.
   Google Scholar
• Ebrahimpour R, Babakhani K, Arani SAAA, et al. Epileptic seizure detection using a neural network ensemble method and wavelet transform. NNW. 2012;22:291–310.
   Web of Science ®Google Scholar
• Guo L, Rivero D, Pazos A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Meth. 2010;193:156–163.
   PubMed Web of Science ®Google Scholar
• Ubeyli ED. Combined neural network model employing wavelet coefficients for EEG signals classification. Digit Signal Process. 2009;19:297–308.
   Web of Science ®Google Scholar
• Zandi AS, Javidan M, Dumont GA, et al. Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEE Trans Biomed Eng. 2010;57:1639–1651.
   PubMed Web of Science ®Google Scholar
• Tzallas AT, Tsipouras MG, Fotiadis DI. Automatic seizure detection based on time-frequency analysis and artificial neural networks. 2007;2007:80510.
   Google Scholar
• Mohseni H, Maghsoudi A, Kadbi M, et al. Automatic detection of epileptic seizure using time–frequency distributions. In: IET 3rd International Conference on Advances in Medical, Signal and Information Processing, MEDSIP 2006. Vol. 14; 2006.
   Google Scholar
• Adeli H, Zhou Z, Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods. 2003;123:69–87.
   PubMed Web of Science ®Google Scholar
• Marchant BP. Time–frequency analysis for biosystem engineering. Biosyst Eng. 2003;85:261–281.
   Web of Science ®Google Scholar
• Semmlow JL. Biosignal and biomedical image processing: MATLAB-based applications. New York: Marcel Dekker, Inc; 2004.
   Google Scholar
• Kandaswamy A, Kumar CS, Ramanathan RP, et al. Neural classification of lung sounds using wavelet coefficients. Comput Biol Med. 2004;34:523–537.
   PubMed Web of Science ®Google Scholar
• Smith LI. A tutorial on principal components analysis. Cornell University, USA; 2002.
   Google Scholar
• Cao LJ, Chua KS, Chong WK, et al. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing. 2003;55:321–336.
   Web of Science ®Google Scholar
• Duda RO, Hart PE, Strok DG. Pattern classification. 2nd ed.; 2001. p. 20–25.
   Google Scholar
• Fielding AH, Cluster and classification techniques for the biosciences. Cambridge, UK: Cambridge University Press; 2007.
   Google Scholar
• Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inform Theory. 1967;13:21–27.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Guo L, Rivero D, Dorado J, et al. Automatic feature extraction using genetic programming: an application to epileptic. EEG Classif. 2011;38:10425–10436.
   Google Scholar
• EEG database from University of Bonn. [cited 16 June 2013]. Available from: http://www.epileptologiebonn.de/cms/front_content.php?idcat=193
   Google Scholar
• Guler I, Ubeyli ED. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J Neurosci Methods. 2005;148:113–121.
   PubMed Web of Science ®Google Scholar
• Sadati N, Mohseni HR, Magshoudi A. Epileptic Seizure Detection Using Neural Fuzzy Networks. In: Proc. IEEE International Conference on Fuzzy Systems, Vancouver, Canada; 2006. p. 596–600.
   Google Scholar
• Guler I, Ubeyli ED. Multiclass support vector machines for EEG-signals classification. IEEE Trans Inform Technol Biomed. 2007;11:117–126.
   PubMedGoogle Scholar
• Polat K, Gunes S. A novel data reduction method: Distance based data reduction and its application to classification of epileptiform EEG signals. Appl Comput. 2008;200:10–27.
   Google Scholar
• Ocak H. Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Sig Process. 2008;88:1858–1867.
   Web of Science ®Google Scholar
• Mousavi SR, Niknazar M, Vahdat BV, Epileptic Seizure Detection using AR Model on EEG Signals. Biomedical Engineering Conference. CIBEC 2008. Cairo International, 2008 Dec 18-20; Cairo International, 1-4.
   Google Scholar
• Subasi A, Gursoy I. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl. 2010;37:8659–8666.
   Web of Science ®Google Scholar
• Lima CA, Coelho AL, Eisencraft M. Tackling EEG signal classification with least squares support vector machines: a sensitivity analysis study. Comput Biol Med. 2010;40:705–714.
   PubMed Web of Science ®Google Scholar
• Sezer E, Işik H, Saracoglu E. Employment and comparison of different artificial neural networks for epilepsy diagnosis from EEG signals. J Med Syst. 2012;36:347–362.
   PubMed Web of Science ®Google Scholar
• Song Y, Zhang J. Automatic recognition of epileptic EEG patterns via Extreme Learning Machine and multiresolution feature extraction. Expert Syst Appl. 2013;40:5477–5489.
   Web of Science ®Google Scholar
• Chen G. Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features. Expert Syst Appl. 2014;41:2391–2394.
   Web of Science ®Google Scholar
• Gajic D, Djurovic Z, Gligorijevic J, et al. Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis. Front Comput Neurosci. 2015;9:1–16.
   PubMed Web of Science ®Google Scholar
• Guo L, Rivero D, Seoane J, Pazos A. Classification of EEG signals using relative wavelet energy and artificial neural networks. In: Proc. ACM/SIGEVO, Summiton Genetic and Evolutionary Computation, New York, USA; 2009. p. 177–184.
   Google Scholar