  A novel non-stationarity visualization tool known as StationPlot is developed
for deciphering the chaotic behavior of a dynamical time series. A family of
analytic measures enumerating geometrical aspects of the non-stationarity &
degree of variability is formulated by convex hull geometry (CHG) on
StationPlot. In the Euclidean space, both trend-stationary (TS) &
difference-stationary (DS) perturbations are comprehended by the asymmetric
structure of StationPlot's region of interest (ROI). The proposed method is
experimentally validated using EEG signals, where it comprehend the relative
temporal evolution of neural dynamics & its non-stationary morphology, thereby
exemplifying its diagnostic competence for seizure activity (SA) detection.
Experimental results & analysis-of-Variance (ANOVA) on the extracted CHG
features demonstrates better classification performances as compared to the
existing shallow feature based state-of-the-art & validates its efficacy as
geometry-rich discriminative descriptors for signal processing applications.
