Graph-Time Spectral Analysis for Atrial Fibrillation
Miao Sun, Elvin Isufi, Natasja M.S. de Groot, Richard C. Hendriks

TL;DR
This paper introduces a novel graph-time spectral analysis method for atrial fibrillation, integrating spatial and temporal frequency analysis to better understand and process electrogram signals, with promising results on simulated and real data.
Contribution
It proposes a new graph signal processing approach combining graph and short-time Fourier transforms for atrial fibrillation analysis, capturing spatial-temporal frequency variations.
Findings
Spatial variation decreases during atrial fibrillation.
Ventricular activity is smoother over the atrial area.
Method effectively cancels ventricular activity from electrograms.
Abstract
Atrial fibrillation is a clinical arrhythmia with multifactorial mechanisms still unresolved. Time-frequency analysis of epicardial electrograms has been investigated to study atrial fibrillation. However, deeper understanding of atrial fibrillation can be achieved if the spatial dimension can be incorporated. Unfortunately, the physical models describing the spatial relations of atrial fibrillation signals are complex and non-linear; hence, the conventional signal processing techniques to study electrograms in the joint space, time, and frequency domain are less suitable. In this study, we wish to put forward a radically different approach to analyze atrial fibrillation with a higher-level model. This approach relies on graph signal processing to represent the spatial relations between epicardial electrograms and put forward a graph-time spectral analysis for atrial fibrillation. To…
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Taxonomy
TopicsECG Monitoring and Analysis · EEG and Brain-Computer Interfaces · Cardiac electrophysiology and arrhythmias
