From: Epilepsy EEG classification using morphological component analysis
Author | Preprocessing method | Feature used | Classifier | Set | Accuracy [%] |
---|---|---|---|---|---|
Guo et al. [10] | Genetic algorithm | Curve length, standard deviation | KNN | Z vs S | 99.20 |
Siuly et al. [13] | Clustering | 9 temporal features | LS-SVM | Z vs S | 99.90 |
O vs S | 96.30 | ||||
Samiee et al. [55] | Rational DSTFT | 5 time frequency features | MLP | Z vs S | 99.80 |
Hassan et al. [56] | CEEMDAN | 6 spectral features | Boosting | Z vs S | 100.0 |
Rincon et al. [60] | Wavelet transform | Bag of words | SVM | Z vs S | 99.85 |
 | Wavelet coefficient | SVM | Z vs S | 100.0 | |
Proposed work | MCA | fR, EMIFS | SVM | Z vs S | 99.63 |
O vs S | 99.91 | ||||
Chen et al. [11] | DTCWT | Logarithm of FFT spectra | NN | Z, O vs S | 100 |
Proposed work | MCA | fR, EMIFS | SVM | Z, O vs S | 99.11 |