Evaluation and Classification of Atrial Arrhythmias from Signatures in Electrographic Flow Metrics.
6 affiliations
- 1Cortex, Inc., Menlo Park, CA, USA.
- 2Department of Computer Science, Northwestern University, Chicago, IL, USA.
- 3Ablacon, Inc., Zurich, Switzerland.
- 4Department of Cardiac Electrophysiology, Piedmont Heart Institute, Athens, GA, USA.
- 5Department of Cardiac Electrophysiology, Augusta University-University of Georgia, Athens, GA, USA.
- 6Department of Cardiology, Ohio State University Wexner Medical Center, Columbus, OH, USA.
Abstract
In patients undergoing atrial fibrillation (AF) ablation, it can be difficult at times to distinguish between AF, micro-re-entrant atrial flutters (AFLs), and focal atrial tachycardias (ATs). Understanding the mechanism of arrhythmogenesis can guide ablation lesion sets. Electrographic flow (EGF) mapping displays global atrial wavefront propagations in near-real time. Patterns of EGF can show both regions of stable flow direction over time and disorganized electrical wavefronts. In so doing, it can differentiate mechanisms of arrhythmogenesis. The objective of this study was to evaluate the EGF patterns and metrics that may be used to successfully differentiate mechanisms of arrhythmogenesis. A total of 2933 recordings from 151 patients who underwent EGF mapping were analyzed to determine metrics that could distinguish between mechanisms of arrhythmogenesis. These EGF-derived metrics include: (1) electrographic flow consistency (EGFC), (2) flow angle variability (FAV), (3) source activity, (4) atrial cycle length, (5) mean R-R correlation, and (6) disorganization score. The disorganization score varied the most between AF and AFL or AT (P < .0001), while the mean EGFC varied the most between AFL and AT (P < .0001). The mean FAV was found to be strongly correlated with both EGFC (r ² = 0.767; P < .0001) and the disorganization score (r ² = 0.552; P < .0001); therefore, the mean FAV was shown to contain the composite information of the other two metrics. A multilayer perceptron trained and tested on an equal number of recordings in each arrhythmia was also able to accurately determine the rhythm from previously unseen samples with 81% accuracy when given only the five remaining metrics. Additionally, it was 100% successful at distinguishing AF from the more organized rhythms. In conclusion, summative EGF metrics that characterize vector flow provide signature patterns of global atrial wavefront propagations in different atrial arrhythmias, which can be used to successfully determine the mechanisms of atrial arrhythmias.
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BibTeX
@article{castellano2026evaluation,
title = {{Evaluation and Classification of Atrial Arrhythmias from Signatures in Electrographic Flow Metrics.}},
author = {Steven Castellano and Melissa H Kong and Joshua D'Arcy and Dmytro Perekrestenko and Kent R Nilsson and John D Hummel},
journal = {The Journal of innovations in cardiac rhythm management},
year = {2026},
month = jul,
doi = {10.19102/icrm.2026.17075},
url = {https://europepmc.org/article/MED/42899034},
note = {CC BY},
}
RIS
TY - JOUR
TI - Evaluation and Classification of Atrial Arrhythmias from Signatures in Electrographic Flow Metrics.
AU - Steven Castellano
AU - Melissa H Kong
AU - Joshua D'Arcy
AU - Dmytro Perekrestenko
AU - Kent R Nilsson
AU - John D Hummel
PY - 2026
DA - 2026/07/15
JO - The Journal of innovations in cardiac rhythm management
DO - 10.19102/icrm.2026.17075
UR - https://europepmc.org/article/MED/42899034
AB - In patients undergoing atrial fibrillation (AF) ablation, it can be difficult at times to distinguish between AF, micro-re-entrant atrial flutters (AFLs), and focal atrial tachycardias (ATs). Understanding the mechanism of arrhythmogenesis can guide ablation lesion sets. Electrographic flow (EGF) mapping displays global atrial wavefront propagations in near-real time. Patterns of EGF can show both regions of stable flow direction over time and disorganized electrical wavefronts. In so doing, it can differentiate mechanisms of arrhythmogenesis. The objective of this study was to evaluate the EGF patterns and metrics that may be used to successfully differentiate mechanisms of arrhythmogenesis. A total of 2933 recordings from 151 patients who underwent EGF mapping were analyzed to determine metrics that could distinguish between mechanisms of arrhythmogenesis. These EGF-derived metrics include: (1) electrographic flow consistency (EGFC), (2) flow angle variability (FAV), (3) source activity, (4) atrial cycle length, (5) mean R-R correlation, and (6) disorganization score. The disorganization score varied the most between AF and AFL or AT (P < .0001), while the mean EGFC varied the most between AFL and AT (P < .0001). The mean FAV was found to be strongly correlated with both EGFC (r ² = 0.767; P < .0001) and the disorganization score (r ² = 0.552; P < .0001); therefore, the mean FAV was shown to contain the composite information of the other two metrics. A multilayer perceptron trained and tested on an equal number of recordings in each arrhythmia was also able to accurately determine the rhythm from previously unseen samples with 81% accuracy when given only the five remaining metrics. Additionally, it was 100% successful at distinguishing AF from the more organized rhythms. In conclusion, summative EGF metrics that characterize vector flow provide signature patterns of global atrial wavefront propagations in different atrial arrhythmias, which can be used to successfully determine the mechanisms of atrial arrhythmias.
SN - 2156-3977
SN - 2156-3993
ER -