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 |