Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals

Automated Epileptic Seizure Detection Methods: A Review Study

“…For rhythmic discharges, fast Fourier transform based (Polat & Gunes, 2007, 2008a, 2008b, frequency domain Chua, Chandran, Acharya, & Lim, 2008;Gabor, 1998;Iscan, Dokur, & Tamer, 2011;Mousavi, Niknazar, & Vahdat, 2008;Murro et al, 1991;Nigam & Graupe, 2004;Sadati, Mohseni, & Magshoudi, 2006;Srinivasan, Eswaran, & Sriraam, 2005;Ubeyli, 2010a), time-frequency based (Martinez-Vargas, Avendano-Valencia, Giraldo, & Castellanos-Dominguez, 2011;Subasi & Gursoy, 2010;Tzallas, et al, 2007aTzallas, et al, , 2007bTzallas, et al, , 2009 or wavelet based features , 2007Guo, Rivero, Dorado, Rabunal, & Pazos, 2010;Guo, Rivero, Seoane, & Pazos, 2009;Kiymik, Subasi, & Ozcalik, 2004;Lima, Coelho, & Eisencraft, 2010;H. Ocak, 2008;H.…”

“…The classification methods varied from simple threshold (Altunay, Telatar, & Erogul, 2010;Martinez-Vargas, et al, 2011), rule based decisions (Gotman, 1990(Gotman, , 1999, or linear classifiers (Ghosh-Dastidar, Iscan, et al, 2011;Liang, et al, 2010;Subasi & Gursoy, 2010) to ANNs , 2008Mousavi, et al, 2008;Nigam & Graupe, 2004;Srinivasan, et al, 2005Srinivasan, et al, , 2007Tzallas, et al, 2007aTzallas, et al, , 2007bTzallas, et al, , 2009Ubeyli, 2006Ubeyli, , 2009cUbeyli, 2010b) that have a complex shaped decision boundary. Other classification methods have been used using SVMs (Chandaka, et al, 2009;Iscan, et al, 2011;Liang, et al, 2010;Lima, et al, 2010;Subasi & Gursoy, 2010;Ubeyli, 2008a;Ubeyli, 2010a), k-nearest neighbour classifiers (Guo, et al, 2011;Iscan, et al, 2011;Liang, et al, 2010;Orhan, et al, 2011;Tzallas, et al, 2009), quadratic analysis (Iscan, et al, 2011), logistic regression Tzallas, et al, 2009), naive Bayes classifiers (Iscan, et al, 2011;Tzallas, et al, 2009), decision trees (Iscan, et al, 2011;Polat & Gunes, 2007;Tzallas, et al, 2009), Gaussian mixture model …”

The Blood-Brain Barrier and Epilepsy

“…The PSD estimates represent the changes in frequency with respect to time. The classical methods (nonparametric or fast Fourier transform-based methods), model-based methods (autoregressive, moving average, and autoregressive moving average methods), time-frequency methods (short-time Fourier transform, wavelet transform), eigenvector methods (Pisarenko, multiple signal classification, Minimum-Norm) can be used to obtain PSD estimates of the signals (Kay & Marple, 1981;Kay, 1988;Proakis & Manolakis, 1996;Stoica & Moses, 1997;Akay, 1998;Akay et al, 1990;Übeyli, 2009a;Übeyli, 2009b;Übeyli, 2010). The obtained PSD estimates provide the features which are well defining the signals.…”

A Survey of Support Vector Machines with Uncertainties

“…In the biomedical field, SVM have been used to identify physical diseases [7][8][9][10] as well as psychological diseases [11]. Electroencephalography (EEG) signals can also be analyzed using SVM [12][13][14]. Besides these, SVM also applied to protein prediction [15][16][17][18][19] and medical images [20][21][22].…”