Key result
Optimized ECG-GraphNet achieves an ~89% F1 score for classifying normal, supraventricular, and ventricular ectopic beats.
Why the study?
Deep learning has improved ECG analysis, yet accurate classification of arrhythmias remains challenging.
Does ECG-GraphNet accurately classify arrhythmias into normal, supraventricular ectopic, and ventricular ectopic beats in single-lead ECG recordings?
Does ECG-GraphNet accurately classify arrhythmias into normal, supraventricular ectopic, and ventricular ectopic beats in single-lead ECG recordings?
ECG-GraphNet, a novel graph convolutional network, demonstrates high accuracy in classifying arrhythmias from single-lead ECG recordings.
Hypothesis-generating for single-lead ECG classification; prospective validation needed before clinical use.
Background: Deep learning has significantly improved medical diagnostics, particularly in electrocardiogram (ECG) analysis, yet accurate classification of arrhythmias remains challenging. Objective: We propose Electrocardiogram Graph Convolutional Network (ECG-GraphNet), a graph convolutional network designed to classify arrhythmias into 3 types: normal (N), supraventricular ectopic (S), and ventricular ectopic (V) beats. Methods: ECG-GraphNet utilizes a novel graph representation of ECG data in which the P wave, QRS complex, and T wave are modeled as individual nodes. A unique QRS-centered weighted average pooling method is employed to enhance beat-specific feature extraction. We systematically explored various aspects including node features, edge definitions, a data augmentation method, and architecture configuration to determine the optimal model design. Experiments were conducted on 10-second ECG recordings from 328 patients using a single-lead device. Results: The optimized ECG-GraphNet achieved a Macro F1 score of 88.61% in 5-fold cross-validation. Scalability experiments further demonstrated its robustness, with Macro F1 scores of 85.21% and 87.03% across diverse ECG patterns and sizes. Conclusion: Our novel approach and comprehensive analysis underscore the potential advantages of ECG-GraphNet in clinical diagnosis and monitoring.
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Lee et al. (2025) studied Arrhythmia (n=328). ECG-GraphNet was evaluated on Macro F1 score for classifying arrhythmias into normal, supraventricular ectopic, and ventricular ectopic beats. The optimized ECG-GraphNet achieved a Macro F1 score of 88.61% in 5-fold cross-validation for classifying arrhythmias into normal, supraventricular ectopic, and ventricular ectopic beats.
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