ID: 4344886 DEEP LEARNING ANALYSIS OF ELECTROCARDIOGRAMS FOR AUTOMATED DETECTION OF CARDIAC ARRHYTHMIAS IN PEDIATRIC PATIENTS

Pediatric arrhythmias are often difficult to detect due to subtle waveform variations and age-specific physiological differences, contributing to diagnostic variability & delay. There is increasing interest in leveraging artificial intelligence (AI) to provide consistent, automated, and accurate interpretation of electrocardiograms (ECGs). This study aimed to develop and validate a convolutional neural network (CNN) model for the automated classification of arrhythmias in pediatric patients using 12-lead ECGs.