Evaluation of Deep Learning Models for LBBB Classification in ECG Signals

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

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arXiv · 2508.02710

Evaluation of Deep Learning Models for LBBB Classification in ECG Signals

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

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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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Beatriz Macas Ordóñez, Diego Vinicio Orellana Villavicencio, José Manuel Ferrández, Paula Bonomini. 2025-07-30. Evaluation of Deep Learning Models for LBBB Classification in ECG Signals. https://arxiv.org/abs/2508.02710

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