Epilepsy EEG classification using morphological component analysis

EURASIP Journal on Advances in Signal Processing

Table 4 Comparison with other works on Bonn dataset for classification between healthy nonseizure set O, Z and seizure or ictal set S

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
Siuly et al. [13]	Clustering	9 temporal features	LS-SVM	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
Rincon et al. [60]		Wavelet coefficient	SVM	Z vs S	100.0
Proposed work	MCA	f_R, E_MIFS	SVM	Z vs S	99.63
Proposed work	MCA	f_R, E_MIFS	SVM	O vs S	99.91
Chen et al. [11]	DTCWT	Logarithm of FFT spectra	NN	Z, O vs S	100
Proposed work	MCA	f_R, E_MIFS	SVM	Z, O vs S	99.11