Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional Networks
arxiv(2024)
摘要
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.
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