Advancing Few-Shot Pediatric Arrhythmia Classification with a Novel Contrastive Loss and Multimodal Learning

• URL: http://arxiv.org/abs/2509.19315v1
• Date: Wed, 10 Sep 2025 05:24:00 GMT
• Title: Advancing Few-Shot Pediatric Arrhythmia Classification with a Novel Contrastive Loss and Multimodal Learning
• Authors: Yiqiao Chen, Zijian Huang, Zhenghui Feng,
• Abstract summary: We propose a multimodal end-to-end deep learning framework that combines dual-branch convolutional encoders for ECG and IEGM.<n>We also introduce a new contrastive loss fucntion named Adaptive Global Class-Aware Contrastive Loss.
• Score: 0.9225176258772595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pediatric arrhythmias are a major risk factor for disability and sudden cardiac death, yet their automated classification remains challenging due to class imbalance, few-shot categories, and complex signal characteristics, which severely limit the efficiency and reliability of early screening and clinical intervention. To address this problem, we propose a multimodal end-to-end deep learning framework that combines dual-branch convolutional encoders for ECG and IEGM, semantic attention for cross-modal feature alignment, and a lightweight Transformer encoder for global dependency modeling. In addition, we introduce a new contrastive loss fucntion named Adaptive Global Class-Aware Contrastive Loss (AGCACL) to enhance intra-class compactness and inter-class separability through class prototypes and a global similarity matrix. To the best of our knowledge, this is the first systematic study based on the Leipzig Heart Center pediatric/congenital ECG+IEGM dataset, for which we also provide a complete and reproducible preprocessing pipeline. Experimental results demonstrate that the proposed method achieves the overall best performance on this dataset, including 97.76\% Top-1 Accuracy, 94.08\% Macro Precision, 91.97\% Macro Recall, 92.97\% Macro F1, and 92.36\% Macro F2, with improvements of +13.64, +15.96, +19.82, and +19.44 percentage points over the strongest baseline in Macro Precision/Recall/F1/F2, respectively. These findings indicate that the framework significantly improves the detectability and robustness for minority arrhythmia classes, offering potential clinical value for rhythm screening, pre-procedural assessment, and postoperative follow-up in pediatric and congenital heart disease populations.

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