Online Graph Learning via Time-Vertex Adaptive Filters: From Theory to Cardiac Fibrillation
We propose AdaCGP, an online algorithm that adaptively estimates graph shift operators from streaming multivariate time series using time-vertex autoregressive models. The method achieves 82% improvement over baseline adaptive VAR models in graph structure recovery and demonstrates clinical utility in tracking ventricular fibrillation dynamics under anti-arrhythmic treatment.