Evaluation of Deep Learning Models for LBBB Classification in ECG Signals

2025-07-30

AI Summary

This study addresses the clinical challenge of accurately differentiating left bundle branch block (LBBB) from strict LBBB (sLBBB) in patients undergoing cardiac resynchronization therapy (CRT) candidate selection. We propose a deep learning–based electrocardiogram (ECG) spatiotemporal feature modeling framework for three-class classification (healthy, LBBB, sLBBB). Systematic evaluation compares convolutional neural networks (CNNs), recurrent neural networks (RNNs), Transformers, and hybrid architectures; our novel design integrates multi-scale temporal modeling with channel-wise attention to enhance discriminative capability for pathological ECG morphologies. On public ECG datasets, the optimal model achieves 92.3% accuracy and a macro-F1 score of 0.94—substantially outperforming conventional metrics (e.g., QRS duration) and baseline models. The approach delivers clinically interpretable, automated sLBBB subtyping, offering a practical, objective tool to improve CRT patient selection efficiency and decision consistency.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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).

Problem

• Evaluating deep learning models for LBBB classification in ECG signals
• Classifying ECG signals into healthy, LBBB, and sLBBB groups
• Optimizing CRT candidate selection via improved LBBB classification

Innovation

• Deep learning models for ECG classification
• Neural networks extract spatial-temporal patterns
• Optimizes LBBB classification for CRT