Table I.

Summary of signal processing techniques

Method	Equation	Variable Definition	Description	References
Dominant Frequency (DF)	$\mathit{DF}=\arg \underset{\mathrm{f}}{max}(\mathrm{X}(\mathrm{f}))$	X(f); spectrum of the voltage intensity time series	Calculates the peak frequency in the signal spectrum (0–30 Hz) of the voltage intensity time series.	11–14
Multiscale Frequency (MSF)	$\mathit{MSF}={\rho }_o{\left[{\displaystyle \sum _{i=1}^{N-1}{q}_i}\right]}^{-1}{\displaystyle \sum _{i=1}^{N=1}{2}^{i+0.5}{q}_{i+1}}$	ρ: local MSF estimate qi: output of the ith log-Gabor filter ρo: center frequency of the first log-Gabor filter	Calculates the instantaneous frequency using the signal spectrum (0–30 Hz) of the voltage intensity time series	24
Shannon Entropy (SE)	$\mathit{SE}={\displaystyle \sum _{i=0}^{N-1}{p}_i{\log }_2{p}_i}$	N: number of amplitude bins ρ: probability of any sample falling within a particular amplitude bin	Quantifies the uncertainty in the voltage intensity distribution	20, 23
Kurtosis (Kt)	$\mathit{Kt}=E\left\{{\left(\frac{x(t)-E\{x(t)\}}{{\sigma }_x}\right)}^4\right\}$	E: expected value x(t): voltage intensity time series σx: variance of the voltage intensity time serie	Quantifies the ‘peakedness’ of the voltage intensity distribution	25
Multiscale Entropy (MSE)	$\mathit{MSE}=-\log \left(\frac{A}{B}\right)$	A: number of matched vector pairs of length m+1 from the moving average time series B: number of matched vector pairs of length m from the moving average time series	Calculates the moving average of the voltage intensity time series and quantifies the regularity and repetitiveness of the data	26