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Related Experiment Video

Updated: Oct 14, 2025

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Extrapolation of Ventricular Activation Times From Sparse Electroanatomical Data Using Graph Convolutional Neural

Felix Meister1,2, Tiziano Passerini3, Chloé Audigier2

  • 1Pattern Recognition Lab, Friedrich-Alexander University, Erlangen, Germany.

Frontiers in Physiology
|November 4, 2021
PubMed
Summary
This summary is machine-generated.

Graph convolutional neural networks estimate ventricular tachycardia activation times from sparse data. This AI approach accurately reconstructs biventricular activation, improving mapping efficiency and reducing measurement needs.

Keywords:
cardiac computational modelingdeep learningelectroanatomic mappinggraph convolutional networkssparse measurements

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Area of Science:

  • Computational electrophysiology
  • Artificial intelligence in medicine
  • Cardiac electrophysiology

Background:

  • Electroanatomic mapping is crucial for ventricular tachycardia assessment but faces challenges in achieving high resolution.
  • Current interpolation methods struggle to reconstruct complete biventricular activation times.
  • Accurate and dense mapping is essential for effective tachycardia treatment.

Purpose of the Study:

  • To investigate the use of graph convolutional neural networks (GCNNs) for estimating biventricular activation times from sparse electroanatomic measurements.
  • To develop an AI-driven method that overcomes limitations of traditional mapping techniques.
  • To improve the accuracy and efficiency of cardiac activation mapping.

Main Methods:

  • Training a GCNN on over 15,000 synthetic ventricular depolarization patterns generated by a computational model.
  • Utilizing diverse anatomical geometries and simulated physiological conditions (scar, conduction velocity variations).
  • Validating the GCNN on independent synthetic data and experimental datasets (porcine and swine hearts).

Main Results:

  • The GCNN accurately reconstructed biventricular activation times in synthetic data with a mean absolute error (MAE) of 3.9 ms ± 4.2 ms at 1 sample/cm².
  • Experimental data showed MAE < 10 ms, even with limited input measurements.
  • A model-guided measurement strategy reduced data requirements by 40% while maintaining accuracy.

Conclusions:

  • GCNNs offer a powerful tool for estimating biventricular activation times, surpassing traditional methods in accuracy and speed.
  • The developed AI approach significantly enhances electroanatomic mapping for ventricular tachycardia.
  • Real-time uncertainty estimation enables optimized data acquisition, improving clinical workflow efficiency.