[Retracted] Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Table 1

value of extracted features from abnormal EMG signals.

Extracted time domain features	value	Extracted time-frequency feature	WVT	SPWVT
			value	value
Enhanced mean absolute value	0.0001	Autocorrelation	0.0001	0.27
Enhanced wavelength	0.002	Cluster prominence	0.0191	0.2675
Mean absolute value	0.0001	Cluster shade	0.0897	0.268
Wavelength	0.0186	Contrast	0.0057	0.2611
Zero crossing	0.0001	Correlation	0.1465	0.3197
Slope sign change	0.0023	Difference entropy	0.0099	0.2301
Root mean square	0.0001	Difference variance	0.0007	0.2248
Average amplitude change	0.0186	Dissimilarity	0.0442	0.2611
Difference absolute standard deviation error	0.3737	Energy	0.0083	0.2031
Log detector	0.37	Entropy	0.1577	0.2002
Modified mean absolute value	0.0001	Homogeneity	0.0946	0.261
Modified mean absolute value 2	0.0018	Information measure of correlation 1	0.9769	0.101
Myopulse percentage rate	0.0001	Information measure of correlation 2	0.4707	0.0891
Simple square integral	0.0346	Inverse difference	0.1315	0.261
Variance of EMG	0.0346	Maximum probability	0.0007	0.2196
Willison amplitude	0.022	Sum average	0.0001	0.1934
Maximal fractal length	0.0599	Sum entropy	0.0899	0.1862
		Sum of squares variance	0.0469	0.198
		Sum variance	0.0368	0.1572

Statistically significant features.