Evaluation of Deep Learning Models for LBBB Classification in ECG Signals

Beatriz Macas Ordóñez, Diego Vinicio Orellana Villavicencio, José Manuel Ferrández, Paula Bonomini
7/30/2025
eess.SPcs.AIcs.LG

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Abstract

This study explores different neural network architectures to evaluate their ability to extract spatial and temporal patterns from electrocardiographic (ECG) signals and classify them into three groups: healthy subjects, Left Bundle Branch Block (LBBB), and Strict Left Bundle Branch Block (sLBBB). Clinical Relevance, Innovative technologies enable the selection of candidates for Cardiac Resynchronization Therapy (CRT) by optimizing the classification of subjects with Left Bundle Branch Block (LBBB).

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Cite this paper

@article{ordez2025evaluation,
  title  = {Evaluation of Deep Learning Models for LBBB Classification in ECG Signals},
  author = {Beatriz Macas Ordóñez and Diego Vinicio Orellana Villavicencio and José Manuel Ferrández and Paula Bonomini},
  year   = {2025},
  eprint = {2508.02710},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2508.02710v1}
}

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