Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional Networks

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 on the MIT-BIH dataset, we investigated various GCN architectures and preprocessing parameters. The results reveal that GCNs, when integrated with VG and VVG for signal graph mapping, can classify arrhythmias without the need for preprocessing or noise removal from ECG signals. While both VG and VVG methods show promise, VG is notably more efficient. The proposed approach was competitive compared to baseline methods, although classifying the S class remains challenging, especially under the inter-patient paradigm. Computational complexity, particularly with the VVG method, required data balancing and sophisticated implementation strategies. The source code is publicly available for further research and development at https://github.com/raffoliveira/VG_for_arrhythmia_classification_with_GCN.