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Machine Learning Localization of Early Right Ventricular Activation Sites Using QRS Integral Features.

Avery Seagren1, Daniel Lancini2, Zixuan Ni1

  • 1The Department of Chemical, Paper, and Biomedical Engineering, Miami University, Oxford, OH, 45056, USA.

Annals of Biomedical Engineering
|December 1, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models using electrocardiogram (ECG) QRS integrals can accurately pinpoint right ventricular (RV) pacing sites. This non-invasive technique shows promise for guiding RV arrhythmia localization.

Keywords:
Catheter ablationPace-mappingQRS integralRight ventricleSupport vector machine regressionVA origin localization

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Area of Science:

  • Electrophysiology
  • Medical Imaging
  • Machine Learning

Background:

  • Accurate non-invasive localization of right ventricular (RV) arrhythmia origins is a significant challenge in electrophysiology.
  • This study explores the use of machine learning (ML) models with 12-lead ECG QRS integrals to identify early RV activation sites.

Purpose of the Study:

  • To investigate the feasibility of using ML models based on QRS integrals for non-invasive localization of RV arrhythmia origins.
  • To assess the accuracy of different support vector regression (SVR) models in pinpointing RV pacing sites.

Main Methods:

  • A generic RV mesh was created from CT scans.
  • QRS integrals (∫QRS) were computed from ECG leads and used as input for SVR models (RBF and linear kernels).
  • Optimal QRS integration windows were identified using bootstrapped cross-validation, with localization accuracy assessed by Euclidean distance, RMSE, and R2.

Main Results:

  • The RBF SVR model, using an initial 60 ms QRS interval, achieved the lowest mean localization error (9.5 mm in the development set, 14.4 mm in the test set).
  • Linear SVR showed more stable performance across QRS durations but with higher mean errors.
  • Validation cohorts showed similar localization errors between kernels, with no statistically significant differences.

Conclusions:

  • QRS-integral-based SVR models allow for millimeter-scale localization of RV pacing sites using surface ECGs.
  • Nonlinear models offer higher accuracy in complex regions, while linear models provide robustness.
  • These findings highlight the clinical potential of ECG-driven ML for RV arrhythmia localization and complementing traditional mapping.