Online graph topology learning via time-vertex adaptive filters: from theory to cardiac fibrillation
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Graph Signal Processing (GSP) provides a powerful framework for analysing complex, interconnected systems by modelling data as signals on graphs. While recent advances have enabled graph topology learning from observed signals, existing methods often struggle with time-varying systems and real-time applications. To address this gap, we introduce AdaCGP, a sparsity-aware adaptive algorithm for dynamic graph topology estimation from multivariate time series. AdaCGP estimates the Graph Shift Operator (GSO) through recursive update formulae designed to address sparsity, shift-invariance, and bias. Through comprehensive simulations, we demonstrate that AdaCGP consistently outperforms multiple baselines across diverse graph topologies, achieving improvements exceeding 83\% in GSO estimation compared to state-of-the-art methods while maintaining favourable computational scaling properties. Our variable splitting approach enables reliable identification of causal connections with near-zero false alarm rates and minimal missed edges. Applied to cardiac fibrillation recordings, AdaCGP tracks dynamic changes in propagation patterns more effectively than established methods like Granger causality, capturing temporal variations in graph topology that static approaches miss. The algorithm successfully identifies stability characteristics in conduction patterns that may maintain arrhythmias, demonstrating potential for clinical applications in diagnosis and treatment of complex biomedical systems.
Date Issued
2025-08-06
Date Acceptance
2025-07-04
Citation
IEEE Transactions on Signal and Information Processing over Networks, 2025, 11
ISSN
2373-776X
Publisher
Institute of Electrical and Electronics Engineers
Journal / Book Title
IEEE Transactions on Signal and Information Processing over Networks
Volume
11
Copyright Statement
Copyright © 2025 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Subjects
adaptive graph signal processing
cardiac fibrillation
functional connectivity
graph shift operator
Graph topology estimation
multivariate statistical models
time-vertex stochastic process
Publication Status
Published
Date Publish Online
2025-08-06