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

StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures