通过跨导联注意力机制，提升心电图基础模型的表征能力
Foundation Model for Cardiac Time Series via Masked Latent Attention
- 用潜在注意力建模导联间高阶关联，实现导联无关的特征聚合
- 在MIMIC-IV-ECG上预测疾病编码准确率超越独立导联建模方法
- 适合做心电图下游任务的医生与医疗AI研发者
心电图（ECG）是临床中最常见的信号之一，在心血管诊断中具有核心作用。尽管近期基础模型（FMs）在学习可迁移的ECG表示方面展现出潜力，但多数现有预训练方法将导联视为独立通道，未能显式利用其强结构冗余性。本文提出潜注意力掩码自编码器（LAMAE）基础模型，在自监督预训练过程中直接挖掘导联间的结构关联。该方法通过潜注意力机制建模导联间的高阶交互，实现排列不变的特征聚合与导联特异性表示的自适应加权。我们在MIMIC-IV-ECG数据集上提供了实证证据，表明利用导联间连接关系是一种有效的结构化监督形式，能显著提升表征质量与迁移性能。所提方法在预测ICD-10编码任务中表现优异，优于独立导联掩码建模和基于对齐的基线方法。
原文摘要 · Abstract (English)
Electrocardiograms (ECGs) are among the most widely available clinical signals and play a central role in cardiovascular diagnosis. While recent foundation models (FMs) have shown promise for learning transferable ECG representations, most existing pretraining approaches treat leads as independent channels and fail to explicitly leverage their strong structural redundancy. We introduce the latent attention masked autoencoder (LAMAE) FM that directly exploits this structure by learning cross-lead connection mechanisms during self-supervised pretraining. Our approach models higher-order interactions across leads through latent attention, enabling permutation-invariant aggregation and adaptive weighting of lead-specific representations. We provide empirical evidence on the Mimic-IV-ECG database that leveraging the cross-lead connection constitutes an effective form of structural supervision, improving representation quality and transferability. Our method shows strong performance in predicting ICD-10 codes, outperforming independent-lead masked modeling and alignment-based baselines.
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