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  3. Multimodal Patient-specific Identification Of Atrial Flutter Circuits From Ecg Time Series Using Explainable Machine Learning.
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  3. Multimodal Patient-specific Identification Of Atrial Flutter Circuits From Ecg Time Series Using Explainable Machine Learning.

Related Experiment Video

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Multimodal Patient-Specific Identification of Atrial Flutter Circuits From ECG Time Series Using Explainable Machine

Samuel Ruiperez-Campillo1, David Hernando2, Elisa Ramirez2

  • 1Department of Computer ScienceETH Zürich 8092 Zürich Switzerland.

IEEE Journal of Translational Engineering in Health and Medicine
|June 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an interpretable framework using atrial vectorcardiograms to accurately identify atrial flutter mechanisms non-invasively. This approach aids in streamlining ablation planning and improving patient outcomes.

Keywords:
Electrocardiographyelectrophysiologyexplainabilitymedical machine learningvectorial analysis

Related Experiment Videos

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Area of Science:

  • Cardiology
  • Medical Imaging
  • Computational Biology

Background:

  • Surface electrocardiogram (ECG) criteria for atrial flutter (AFL) are often unreliable for pre-procedural mechanism identification.
  • Invasive electrophysiological (EP) studies are typically required to confirm AFL circuit definitions and guide ablation strategies.

Purpose of the Study:

  • To develop and evaluate an interpretable, non-invasive framework for stratifying AFL mechanisms.
  • To improve the accuracy of pre-procedural AFL identification to streamline mapping and ablation planning.

Main Methods:

  • Analysis of 97 patients undergoing EP study with simultaneous 12-lead ECG.
  • Reconstruction of atrial vectorcardiograms (VCGs) and extraction of interpretable loop morphology and kinematic descriptors.
  • Development of an explainable tree-ensemble model integrating VCG descriptors and clinical variables.

    • Main Results:

    • VCG loops demonstrated subtype-specific archetypes for different AFL mechanisms.
    • The multimodal Random-Forest model achieved high discrimination (AUROC 0.790-0.900) across AFL subtypes.
    • The model showed high sensitivity for common AFL and very high specificity for perimitral clockwise AFL.

    Conclusions:

    • The developed interpretable framework offers a practical, non-invasive method for mechanism-oriented AFL stratification.
    • This approach supports targeted mapping and more efficient ablation planning for atrial flutter.
    • Future research will focus on multicenter validation and broadening applicability by removing adenosine dependency.