Table II.

An ephemeral summary and assessment of the classification accuracy of the proposed work with the existing work.

Authors	Methods	Features	Classifier	Accuracy %
Karimi et al. [39 40]	DWT, HSS and WPT	Several statistical features	ANN	90
Lee et al. [12]	Nonlinear methods, Linear, and reduced features by t-test	5 nonlinear features and 6 Linear	SVM	90
Babak et al. [35]	Linear, non linear methods and features reduced by GDA and LDA	7 time domain, 1 frequency and 7 non-linear	SVM	95.77
Zhao and Ma [42 43]	EMD, and TEO	Several statistical features	BPNN	85
Babaoglu et al. [43 44]	GA, EST and BPSO	11 Features	SVM	81.46
Babaoglu et al. [44 45]	EST and features reduced by PCA	18 Features	SVM	79.17
Dua et al. [40 41]	Features reduced by PCA	6 Nonlinear features	MLP	89.5
Giri et al. [11]	DWT, features reduced by ICA,	10 Features	GMM	96.8
Patidar et al. [41 42]	TQWT and features reduced by PCA	2 Entropy features	LS-SVM with Morlet wavelet kernel	99.72
Nan Liu et al. [45 46]	Selective and total segments	Linear and frequency feat	ELM & SVM	68.48 & 71.20
Mohit et al. [32]	FAWT and ranking method like Entropy, ROC and Bhattacharya	FzEn and K-NN Entropy estimator	LS-SVM with RBF & Morlet wavelet kernel	100
Monappa et al. [4]	Linear and non-linear, features reduced by PCA	Various time domain, Frequency domain and non-linear domain	PNN, KNN and SVM	Without PCA: 68.33, 76.67 and 90.00 With PCA: 68.33, 85.00 and 91.67
This work	MSWP transform, Fisher ranking method and features reduced by GDA,LDA	31 features by K-NNE & 31 features by FZE method.	ELM	100