StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures
This work addresses seizure detection in epilepsy patients, presenting an incremental improvement with a new visualization and feature extraction method for EEG signal processing.
The authors tackled the problem of detecting epileptic seizures by developing StationPlot, a novel non-stationarity visualization tool, and demonstrated its efficacy through experimental validation on EEG signals, showing better classification performance compared to existing shallow feature-based state-of-the-art methods.
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.