Graph-time spectral analysis for atrial fibrillation

Research output: Contribution to journalArticleScientificpeer-review

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 can be achieved by incorporating the spatial dimension. Unfortunately, the physical models describing the spatial relations of atrial fibrillation signals are complex and non-linear; hence, 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. To capture the frequency content along both the time and graph domain, we propose the joint graph and short-time Fourier transform. The latter allows us to analyze the spatial variability of the electrogram temporal frequencies. With this technique, we found the spatial variation of the atrial electrograms decreases during atrial fibrillation since the high temporal frequencies of the atrial waves reduce. The proposed analysis further confirms that the ventricular activity is smoother over the atrial area compared with the atrial activity. Besides using the proposed graph-time analysis to conduct a first study on atrial fibrillation, we demonstrate its potential by applying it to the cancellation of ventricular activity from the atrial electrograms. Experimental results on simulated and real data further corroborate our findings in this atrial fibrillation study.

Original languageEnglish
Article number101915
Number of pages11
JournalBiomedical Signal Processing and Control
Volume59
DOIs
Publication statusPublished - 1 May 2020

Keywords

  • Atrial activity extraction
  • Atrial fibrillation
  • Graph signal processing
  • Graph-time signal processing
  • Spectral analysis

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