Evaluation of Deep Learning Models for LBBB Classification in ECG Signals

Beatriz Macas Ordóñez; Diego Vinicio Orellana Villavicencio; José Manuel Ferrández; Paula Bonomini

Evaluation of Deep Learning Models for LBBB Classification in ECG Signals

Signal Processing 2025-08-06 v1 Artificial Intelligence Machine Learning

Authors: Beatriz Macas Ordóñez , Diego Vinicio Orellana Villavicencio , José Manuel Ferrández , Paula Bonomini

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).

Keywords

ecg signal processing eeg signal processing seizure detection and eeg analysis

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@article{arxiv.2508.02710,
  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},
  journal= {arXiv preprint arXiv:2508.02710},
  year   = {2025}
}

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Accepted for presentation in the 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2025)

Evaluation of Deep Learning Models for LBBB Classification in ECG Signals

Abstract

Keywords

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