Evaluation of Deep Learning Models for LBBB Classification in ECG Signals
By: Beatriz Macas Ordóñez , Diego Vinicio Orellana Villavicencio , José Manuel Ferrández and more
Potential Business Impact:
Finds heart problems to help sick hearts get better.
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).
Similar Papers
Heart Rate Classification in ECG Signals Using Machine Learning and Deep Learning
Signal Processing
Finds heart problems from heartbeat signals.
BEAT-Net: Injecting Biomimetic Spatio-Temporal Priors for Interpretable ECG Classification
Machine Learning (CS)
Reads heart signals like a doctor reads words.
Transferring Clinical Knowledge into ECGs Representation
Machine Learning (CS)
Makes heart monitors understand sickness better.