Arrhythmogenic right ventricular cardiomyopathy (ARVC) and long QT syndrome (LQTS) are inherited arrhythmia syndromes associated with sudden cardiac death. Deep learning shows promise for ECG interpretation, but multi-class inherited arrhythmia classification with clinically grounded interpretability remains underdeveloped. Our objective was to develop and validate a lead-aware deep learning framework for multi-class (ARVC vs LQTS vs control) and binary inherited arrhythmia classification, and to determine optimal strategies for integrating ECG foundation models within arrhythmia screening tools. We assembled a 13-center Canadian cohort (645 patients; 1,344 ECGs). We evaluated four ECG foundation models using three transfer learning approaches: linear probing, fine-tuning, and combined strategies. We developed lead-aware spatial attention networks (LASAN) and assessed integration strategies combining LASAN with foundation models. Performance was compared against the established foundation model baselines. Lead-group masking quantified disease-specific lead dependence. Fine-tuning outperformed linear probing and combined strategies across all foundation models (mean macro-AUROC 0.904 vs 0.825). The best lead-aware integrations achieved near-ceiling performance (HuBERT-ECG hybrid: macro-AUROC 0.990; ARVC vs control AUROC 0.999; LQTS vs control AUROC 0.994). Lead masking demonstrated physiologic plausibility: V1-V3 were most critical for ARVC detection (4.54% AUROC reduction), while lateral leads were preferentially important for LQTS (2.60% drop). Lead-aware architectures achieved state-of-the-art performance for inherited arrhythmia classification, outperforming all existing published models on both binary and multi-class tasks while demonstrating clinically aligned lead dependence. These findings support potential utility for automated ECG screening pending validation.

翻译：致心律失常性右室心肌病（ARVC）与长QT综合征（LQTS）是两种与心源性猝死相关的遗传性心律失常综合征。深度学习在心电图（ECG）判读中展现出潜力，但兼具临床可解释性的多类别遗传性心律失常分类方法仍待发展。本研究旨在开发并验证一种用于多类别（ARVC vs LQTS vs 对照）及二分类遗传性心律失常分类的导联感知深度学习框架，并确定在心律失常筛查工具中整合ECG基础模型的最佳策略。我们构建了一个包含13个中心的加拿大队列（645名患者；1,344份ECG）。采用三种迁移学习策略（线性探测、微调及组合策略）评估了四种ECG基础模型。我们开发了导联感知空间注意力网络（LASAN），并评估了将LASAN与基础模型结合的集成策略。性能与已建立的基础模型基线进行了比较。通过导联组掩蔽量化了疾病特异性的导联依赖性。在所有基础模型中，微调策略均优于线性探测与组合策略（平均宏观AUROC 0.904 vs 0.825）。最佳的导联感知集成方案实现了接近上限的性能（HuBERT-ECG混合模型：宏观AUROC 0.990；ARVC vs 对照 AUROC 0.999；LQTS vs 对照 AUROC 0.994）。导联掩蔽实验显示出生理合理性：V1-V3导联对ARVC检测最为关键（AUROC降低4.54%），而侧壁导联对LQTS尤为重要（AUROC降低2.60%）。导联感知架构在遗传性心律失常分类任务中取得了最先进的性能，在二分类与多分类任务上均优于所有已发表的模型，同时展示了与临床一致的导联依赖性。这些发现支持了该框架在自动化ECG筛查中的潜在应用价值，有待进一步验证。