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Table 3 Comparison of set F, N vs S results with other existing works on Bonn dataset

From: Epilepsy EEG classification using morphological component analysis

Author

Preprocessing method

Feature used

Classifier

Set

Accuracy [%]

Liang et al. [4]

Fast Fourier transform

16 spectral features

SVM

F vs S

98.74

Siuly et al. [13]

Clustering

9 temporal features

LS-SVM

F vs S

93.91

N vs S

97.69

Riaz et al. [21]

EMD

6 temporal and spectral features

Decision trees

F vs S

96.00

SVM

F vs S

93.00

Samiee et al. [55]

Rational DSTFT

5 time frequency features

MLP

F vs S

94.90

N vs S

98.50

Hassan et al. [56]

CEEMDAN

6 spectral features

Boosting

F vs S

97.00

N vs S

100.0

SVM

F vs S

93.00

N vs S

99.00

Proposed work

MCA

\( {\sigma}_T^2 \), <ω>2

SVM

F vs S

97.13

N vs S

99.78

Sharma et al. [25]

EMD

2D, 3D, PSR

LS-SVM

F, N vs S

98.67

Altunay et al. [57]

L. P Filter

Energy feature

Threshold

F, N vs S

94.00

Joshi et al. [58]

FLP

FLP energy, signal energy

SVM

F, N vs S

95.33

Pachori et al. [59]

EMD

SODP of IMF

ANN

F, N vs S

97.75

Proposed work

MCA

\( {\sigma}_T^2 \), <ω>2

SVM

F, N vs S

93.61

  1. RDSTFT rational discrete STFT, CEEMDAN complete ensemble empirical mode decomposition with adaptive noise, PSR phase space representation, L. P Filter linear prediction filter, FLP fractional linear prediction