Optimizing beat-wise input for arrhythmia detection using 1-D convolutional neural networks: A real-world ECG study.
Journal:
Computer methods and programs in biomedicine
Published Date:
Jun 18, 2025
Abstract
BACKGROUNDS AND OBJECTIVES: Cardiac arrhythmias, characterized by irregular heartbeats, are difficult to diagnose in real-world scenarios. Machine learning has advanced arrhythmia detection; however, the optimal number of heartbeats for precise classification remains understudied. This study addresses this using machine learning while assessing the performance of arrhythmia detection across inter-patient and intra-patient conditions. Furthermore, the performance-resource trade-offs are evaluated for practical deployment in mobile health (mHealth) applications.
