Towards Finite-Time Consensus with Graph Convolutional Neural Networks

A. Iancu, E. Isufi

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Atrial electrograms are often used to gain understanding on the development of atrial fibrillation (AF). Using such electrograms, cardiologists can reconstruct how the depolarization wave-front propagates across the atrium. Knowing the exact moment at which the depolarization wavefront in the tissue reaches each electrode is an important aspect of such reconstruction. A common way to determine the LAT is based on the steepest deflection (SD) of the individual electrograms. However, the SD annotates each electrogram individually and is expected to be more prone to errors compared to approaches that would employ the data from the surrounding electrodes to estimate the LAT. As electrograms from neighboring electrodes tend to have rather similar morphology up to a delay, we propose in this paper to use the cross-correlation to find the pair-wise relative delays between electrograms. Instead of only using the direct neighbors we consider the array as a graph and involve higher order neighbors as well. Using a least-squares method, the absolute LATs can then be estimated from the calculated pair-wise relative delays. Simulated and clinically recorded electrograms are used to evaluate the proposed approach. From the simulated data it follows that the proposed approach outperforms the SD approach.
Original languageEnglish
Title of host publication28th European Signal Processing Conference (EUSIPCO 2020)
Place of PublicationAmsterdam (Netherlands)
Number of pages5
ISBN (Electronic)978-9-0827-9705-3
Publication statusPublished - 1 Aug 2020
EventEUSIPCO 2020: The 28th European Signal Processing Conference - Amsterdam, Netherlands
Duration: 18 Jan 202122 Jan 2021
Conference number: 28th


ConferenceEUSIPCO 2020
OtherDate change due to COVID-19 (former date August 24-28 2020)


  • Finite-time consensus,
  • graph convolutions
  • graph signal processing
  • graph neural networks


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