Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix
This work addresses arrhythmia classification in ECG signal analysis, representing an incremental improvement over existing methods.
The study tackled arrhythmia classification by generating an adjacency matrix from correlation matrices of extracted features and applying a graph neural network, achieving precision and recall exceeding 50% for all classes.
With the advancements in graph neural network, there has been increasing interest in applying this network to ECG signal analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted features and applied a graph neural network to classify arrhythmias. The proposed model was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that this method can be considered an approach for arrhythmia classification.