Cross-wavelet transform as a new prototype for classification of EEG signals

References

• Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H. 2017. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine, doi:10.1016/j.compbiomed.2017.09.017
   Google Scholar
• Andrzejak R.G., et al. 2001. Indication of nonlinear deterministic and finite dimensional structure in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E. Vol. 64, DOI: 10.1103/PhysRevE.64.061907.
   Web of Science ®Google Scholar
• Bajaj V, Pachori R. 2012. Classification of seizure and non-seizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed. 16(6):1135–1142.
   PubMedGoogle Scholar
• Banerjee S, Mitra M. 2014 February. Application of cross wavelet transform for ECG pattern analysis and classification. IEEE Trans Instrum Meas. 63(2).
   Web of Science ®Google Scholar
• Bhati D, Sharma M, Pachori RB, Gadre VM. 2017 March. Time-frequency localized three- band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification. Digital Signal Processing. 62:259–273.
   Web of Science ®Google Scholar
• Bhattacharyya A, Pachori RB, Upadhyay A, Acharya UR. 2017. Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl Sci. 7:385.
   Google Scholar
• Debangshu Dey, Chaudhari Rajendra R., Mukhopadhyay  Achintya, Sen Swarnendu and  Chakravorti  Sivaji. 2014. A cross-wavelet transform aided rule based approach for early prediction of lean bloe-out in swirl-stabilized dump combustor. International Journal Spray Combustion Dynamics. 7:69–90.
   Web of Science ®Google Scholar
• Dhar P, Dutta S, Das P, Mukherjee V. 2018. Cross-wavelet aided ECG beat classification using LIBSVM. Computer Methods in Biomechanics and Biomedical Engineering: Imaging& Visualization. 6(3): 343–352.
   Web of Science ®Google Scholar
• Djemili R, Bourouba H, Korba MCA. 2016. Appication of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals,”. Biocybernetics Biomed Eng. 36:285–291.
   Web of Science ®Google Scholar
• Dutta S, Chatterjee A, Munshi S. 2011. Identification of ECG beats from cross-spectrum information aided learning vector quantization. Meas. 44:2020–2027.
   Web of Science ®Google Scholar
• Ghosh-Dastidar S, Adeli H. 2008 February. Pricipal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans Biomed Eng. 55(2).
   PubMed Web of Science ®Google Scholar
• Grinsted A, Moore JC, Jevrejeva S. 2004. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys. 11:561–566.
   Web of Science ®Google Scholar
• Kannathal N, Min Lim Choo U, Acharya R, Sadasivan PK. 2005. Entropies for detection of epilepsy in EEG. Comput Methods Programs Biomed. 80:187–194.
   PubMed Web of Science ®Google Scholar
• Kohonen T. 1990. The self-organizing map. Proc IEE, 78(9): 1464–1480.
   Web of Science ®Google Scholar
• Nicolaou N, Georgiue J. 2012. Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst Appl. 39:202–209.
   Web of Science ®Google Scholar
• Orhan U, Hekim M, Ozer M. 2011. EEG signal classification using K-means clustering and a multilayer perceptron neural network model. Expert Syst with Appl. 38:13475–13481.
   Web of Science ®Google Scholar
• Pachori RB, Patidar S. 2014. Epileptic seizure classification in EEG signals using second order difference plot of intrinsic mode functions,”. Comput Methods Programs Biomed. 113:494–502.
   PubMed Web of Science ®Google Scholar
• Pachori RB. 2008 December. Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res Lett Signal Process. 2008: Article ID 293056, 5.
   Google Scholar
• Pachori RB, Bajaj V. 2011 December. Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Comput Methods Programs Biomed. 104(3):373–381.
   PubMed Web of Science ®Google Scholar
• Rajendra Acharya U, Molinari F, Vinitha Sree S, Chattopadhyay S, Ng K-H, Suri JS. 2012a. Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control. 7:401–408.
   Web of Science ®Google Scholar
• Rajendra Acharya U, Vinitha Sree S, Alvin APC, Suri JS. 2012b. Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework. Expert Syst with Appl. 39:9072–9078.
   Web of Science ®Google Scholar
• Ruessink BG, Coco G, Ranasinghe R, Turner IL. 2006. A cross-wavelet study of alongshore nonuniform nearshore sandbar behavior. International Joint Conference on Neural Networks Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada; 2006 Jul 16–21.
   Google Scholar
• Sharma M, Pachori RB. 2017 November. A novel approach to detect epileptic seizures using a combination of tunable-Q wavelet transform and fractal dimension. J Mech Med Biol. 17(7): 1740003, 20.
   Web of Science ®Google Scholar
• Sharma M, Pachori RB, Acharya UR. 2017 July. A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognit Lett. 94:172–179.
   Web of Science ®Google Scholar
• Specht. DF. 1990. Probabilistic neural networks. Neural Networks. 3(1):109–118.
   Web of Science ®Google Scholar
• Subasi A. 2007. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst with Appl. 32:1084–1093.
   Web of Science ®Google Scholar
• Subasi A, Ismail Gursoy M. 2010. EEG signal classification using PCA,ICA,LDA and support vector machines,”. Expert Syst with Appl. 37:8659–8666.
   Web of Science ®Google Scholar
• Sunil Kumar T, Kanhangad V, Pachori RB. 2015. Classification of seizure and seizure free EEG signals using local binary patterns. Biomed Signal Process Control. 15:33–40.
   Web of Science ®Google Scholar
• Suykens JAK, Vandewalla J. 1999. Least square support vector machine classifiers. Neural Process Lett. 9(3):293–300.
   Web of Science ®Google Scholar
• Torrence C, Webster PJ. 1998. Interdecadal changes in the ENSO–monsoon System. J Clim. 12:2679–2690.
   Web of Science ®Google Scholar
• Tzallas AT, Tsipouras MG, Fotiadis DI. 2007. Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput Intelligence Neurosci. doi:10.1155/2007/80510
   PubMedGoogle Scholar
• Ubeyli ED. 2008. Wavelet/mixture of experts network structure for EEG signals classification. Expert Syst with Appl. 34:1954–1962.
   Web of Science ®Google Scholar
• Ubeyli ED. 2009. Combined neural network model employing wavelet coefficient for EEG signals classification. Digital Signal Processing. 19:297–308.
   Web of Science ®Google Scholar
• Vipani R, Hore S, Basu S, Basak S, Dutta S. 2017. Identification of epileptic seizures using hibert transform and learning vector quantization based classifier. IEEE Calcutta Conference. (CALCON): ISBN (Online):  978-1-5386-3744-9, 90–94.
   Google Scholar