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Table 7 Comparison of methodologies presented in the literature for the five-class (Z-O-N-F-S) problem

From: Spectral information of EEG signals with respect to epilepsy classification

Authors Feature extraction Classification Validation Classification accuracy
Guler and Ubeyli [32] (2005) DWT (db2)/mean, min, max, std Adaptive neuro-fuzzy inference system 50% holdout 98.68%
Ubeyli and Guler [33] (2007) Eigenvector methods (Pisarenko, MUSIC, Minimum-Norm) Modified mixture-of-experts 50% holdout 98.60%
Tzallas et al. [17] (2009) TFD (SPWVD)/fractional energy ANN Monte Carlo cross-validation (50% split—10 repeats) 89%
Liang et al. [19] (2010) FFT/ApEn SVM Monte Carlo cross-validation (60–40% split—10 repeats) 85.90%
Nicolaou et al. [34] (2012) Permutation entropy SVM Monte Carlo cross-validation (various splits—100 repeats) 86.10%
Murugavel and Ramakrishnan [10] (2014) DWT (db2)/energy, entropy, mean, min, max, std OELM 50% holdout 94%
Tawfik et al. [35] (2016) Weighted permutation entropy SVM 10-fold cross-validation 93.75%
This study Frequency sub-bands/energy, total energy, fractional energy, entropy Random forests 10-fold cross-validation 91.20%