Evaluation of Deep Learning Models for LBBB Classification in ECG Signals
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866915426542288896 |
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| author | Ordóñez, Beatriz Macas Villavicencio, Diego Vinicio Orellana Ferrández, José Manuel Bonomini, Paula |
| author_facet | Ordóñez, Beatriz Macas Villavicencio, Diego Vinicio Orellana Ferrández, José Manuel Bonomini, Paula |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02710 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluation of Deep Learning Models for LBBB Classification in ECG Signals Ordóñez, Beatriz Macas Villavicencio, Diego Vinicio Orellana Ferrández, José Manuel Bonomini, Paula Signal Processing Artificial Intelligence Machine Learning 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). |
| title | Evaluation of Deep Learning Models for LBBB Classification in ECG Signals |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.02710 |