Why the study?
Current deep learning methodologies for automated ECG arrhythmia classification frequently fail to account for physiological rhythms and clinical diagnostic reasoning, compromising reliability and interpretability.
Does the FHGNet framework improve the classification accuracy and interpretability of VT and SVT compared to baseline deep learning models?
Does the FHGNet framework improve the classification accuracy and interpretability of VT and SVT compared to baseline deep learning models?
FHGNet provides a highly accurate (91.35% macro F1-score) and interpretable deep learning framework for automated ECG classification of VT and SVT.
May improve automated VT/SVT ECG classification in practice; extends deep learning baselines with enhanced interpretability.
Automated electrocardiogram (ECG) classification plays a critical role in arrhythmia diagnosis. However, current deep learning-based methodologies frequently fail to account for physiological rhythms and clinical diagnostic reasoning, thereby compromising their reliability and interpretability. This study proposes a clinically inspired multi-lead oscillatory Transformer framework, named FHGNet, to enhance the precision and interoperability of classifying ventricular tachycardia (VT) and supraventricular tachycardia (SVT). The proposed architecture integrates R-peak detection for heartbeat segmentation and adaptive-length patch extraction with R-wave positional encoding to enhance temporal awareness. It employs a convolutional neural network (CNN) to capture intra-beat morphological features (QRS morphology), a Transformer with FANLayer to model inter-beat rhythmic patterns, and a graph attention network (GAT) to fuse multi-lead dependencies. Additionally, a two-stage classifier is designed to enhance the detection of rare arrhythmia classes. Experimental evaluations on the MIT-BIH Supraventricular Arrhythmia dataset demonstrate FHGNet achieves a macro F1-score of 91.35% outperforming baselines. Ablation studies reveal that removing GAT reduces F1 by 2.42% in multi-lead scenarios, while the two-stage design improves minority class recall by 5.82%. Attention visualization confirms the model focuses on clinically relevant features, such as ST-T segment energy ratios and inter-lead phase differences, aligning with established diagnostic criteria. Additionally, the interpretability of FHGNet is further enhanced by two aspects: 1) Explicit integration of physiological priors (e.g., RR interval variability, intra-beat positional information) in dynamic feature engineering, which enables the model to align with clinicians' rhythm analysis logic; 2) The two-stage classifier strictly follows the clinical diagnostic workflow (first screening abnormalities, then subclassifying), making the decision-making process traceable. This work provides an interpretable, clinically adaptive framework for high-accuracy ECG classification, potentially reducing reliance on invasive electrophysiological studies.
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Ju et al. (2026) studied this question.
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