Arrhythmias are a major cause of sudden cardiac death in children, making automated rhythm classification from electrocardiograms (ECGs) clinically important. However, pediatric arrhythmia analysis remains challenging because of age-dependent waveform variability, limited data availability, and a pronounced long-tailed class distribution that hinders recognition of rare but clinically important rhythms. To address these issues, we propose a multimodal end-to-end framework that integrates surface ECG and intracardiac electrogram (IEGM) signals for pediatric arrhythmia classification. The model combines dual-branch feature encoders, attention-based cross-modal fusion, and a lightweight Transformer classifier to learn complementary electrophysiological representations. We further introduce an Adaptive Global Class-Aware Contrastive Loss (AGCACL), which incorporates prototype-based alignment, class-frequency reweighting, and globally informed hard-class modulation to improve intra-class compactness and inter-class separability under class imbalance. We evaluate the proposed method on the pediatric subset of the Leipzig Heart Center ECG-Database and establish a reproducible preprocessing pipeline including rhythm-segment construction, denoising, and label grouping. The proposed approach achieves 96.22% Top-1 accuracy and improves macro precision, macro recall, macro F1 score, and macro F2 score by 4.48, 1.17, 6.98, and 7.34 percentage points, respectively, over the strongest baseline. These results indicate improved minority-sensitive classification performance on the current benchmark. However, further validation under subject-independent and multicenter settings is still required before clinical translation.

翻译：心律失常是导致儿童心源性猝死的主要病因，因此基于心电图（ECG）的自动节律分类具有重要临床意义。然而，儿科心律失常分析仍面临年龄依赖性波形变异、数据可用性有限以及显著的长尾类别分布等挑战——这些因素阻碍了对罕见但临床重要节律的识别。针对上述问题，本文提出了一种融合体表心电图和心内电图（IEGM）信号的多模态端到端框架，用于儿科心律失常分类。该模型结合双分支特征编码器、基于注意力的跨模态融合模块及轻量级Transformer分类器，以学习互补的电生理表征。我们进一步引入自适应全局类感知对比损失（AGCACL），通过原型对齐、类别频率重加权和全局信息驱动的困难类别调制，在类别不平衡条件下提升类内紧凑性与类间可分性。我们在莱比锡心脏中心ECG数据库的小儿子集上评估了所提方法，并建立了包含节律片段构建、去噪和标签分组的可复现预处理流程。该方法在最强基线模型基础上，实现了96.22%的Top-1准确率，宏精确率、宏召回率、宏F1分数和宏F2分数分别提升4.48、1.17、6.98和7.34个百分点。结果表明，当前基准测试中针对少数类别的分类性能已获显著改善。然而，在临床转化前仍需在受试者独立与多中心设置下进行进一步验证。