Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional Networks

arXiv:2404.15367v1 Announce Type: cross
Abstract: Arrhythmias, detectable via electrocardiograms (ECGs), pose significant health risks, emphasizing the need for robust automated identification techniques. Although traditional deep learning methods have shown potential, recent advances in graph-based strategies are aimed at enhancing arrhythmia detection performance. However, effectively representing ECG signals as graphs remains a challenge. This study explores graph representations of ECG signals using Visibility Graph (VG) and Vector Visibility Graph (VVG), coupled with Graph Convolutional Networks (GCNs) for arrhythmia classification. Through experiments …

arxiv classification cs.cv cs.lg eess.sp graph graphs networks type visibility