Evaluation of Deep Learning Models for LBBB Classification in ECG Signals
This work addresses the problem of improving candidate selection for CRT in patients with LBBB, but it appears incremental as it focuses on evaluating existing methods without introducing new ones.
The study evaluated neural network architectures for classifying ECG signals into healthy, LBBB, and sLBBB groups to aid in selecting candidates for Cardiac Resynchronization Therapy, but no concrete results or numbers were provided.
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).