Table 7.

Ten statistical features.

Feature	Definition	Equation
92. Mean	Sum of all data divided by the number of entries	$\overline{x}=\frac{{\sum }^{}x}{n}$
93. Median	Value that is in the middle of the ordered set of data	Odd numbers of entries: Median = middle data entry. Even numbers of entries: Median = adding the two numbers in the middle and dividing the result by two.
94. Standard Deviation	Measure variability and consistency of the sample.	$s$ = $\sqrt{\frac{{\sum }^{}x-\overline{x}}{n-1}}$
95. Percentile	The data value at which the percent of the value in the data set are less than or equal to this value.	25th = ($\frac{25}{100}$)n
		75th = ($\frac{75}{100}$)n
96. Mean Absolute Deviation	Average distance between the mean and each data value.	MAD = $\frac{{\sum }_{i=1}^n\left| x_i-\overline{x }\right|}{n}$
97. Inter Quartile Range (IQR)	The measure of the middle 50% of data.	IQR = Q3–Q1 Q3: Third quartile, Q1: First quartile, Quartile: Dividing the data set into four equal portions.
98. Skewness	The measure of the lack of symmetry from the mean of the dataset.	g1 = $\frac{{\sum }_{i=1}^N{\left(Y_i-Y\right)}^3/N}{S^3}$ Y: Mean, s: Standard deviation, N: Number of data.
99. Kurtosis	The pointedness of a peak in distribution curve, in other words it is the measure of sharpness of the peak of distribution curve.	K = $\frac{{\sum }_{i=1}^N{\left(Y_i-Y\right)}^4/N}{S^4}-3$ Y: Mean, s: Standard deviation, N: Number of data.
100. Shannon’s Entropy	Entropy measures the degree of randomness in a set of data, higher entropy indicates a greater randomness, and lower entropy indicates a lower randomness.	H(x) = −$\sum _{i=0}^{N-1}{p}_i{\log }_2{p}_i$
101. Spectral Entropy	The normalized Shannon’s entropy that is applied to the power spectrum density of the signal.	SEN =$\frac{-{\sum }_{i=0}^{N-1}{p}_{k{\log }_2{p}_k}}{\log N}$ $p_k$: Spectral power of the normalized frequency, N: Number of frequencies in binary