Electrocardiography (ECG) is a low-cost, widely used modality for diagnosing electrical abnormalities like atrial fibrillation by capturing the heart's electrical activity. However, it cannot directly measure cardiac morphological phenotypes, such as left ventricular ejection fraction (LVEF), which typically require echocardiography (Echo). Predicting these phenotypes from ECG would enable early, accessible health screening. Existing self-supervised methods suffer from a representational mismatch by aligning ECGs to single-view Echos, which only capture local, spatially restricted anatomical snapshots. To address this, we propose Echo2ECG, a multimodal self-supervised learning framework that enriches ECG representations with the heart's morphological structure captured in multi-view Echos. We evaluate Echo2ECG as an ECG feature extractor on two clinically relevant tasks that fundamentally require morphological information: (1) classification of structural cardiac phenotypes across three datasets, and (2) retrieval of Echo studies with similar morphological characteristics using ECG queries. Our extracted ECG representations consistently outperform those of state-of-the-art unimodal and multimodal baselines across both tasks, despite being 18x smaller than the largest baseline. These results demonstrate that Echo2ECG is a robust, powerful ECG feature extractor. Our code is accessible at https://github.com/michelleespranita/Echo2ECG.

翻译：心电图（ECG）是一种低成本、广泛应用的诊断方式，通过捕捉心脏电活动来检测房颤等电生理异常。然而，它无法直接测量左心室射血分数（LVEF）等心脏形态学表型，这些表型通常需要超声心动图（Echo）评估。从ECG预测这些表型有助于实现早期、便捷的健康筛查。现有自监督方法存在表征错配问题——它们将ECG与单视角超声心动图对齐，后者仅能捕获局部、空间受限的解剖快照。为解决此问题，我们提出Echo2ECG，一种多模态自监督学习框架，利用多视角超声心动图中捕获的心脏形态学结构丰富ECG表征。我们在两项根本上依赖形态学信息的临床任务中评估了Echo2ECG作为ECG特征提取器的性能：（1）三个数据集上的结构性心脏表型分类任务，以及（2）利用ECG查询检索具有相似形态学特征的超声心动图研究。我们的ECG表征在这两项任务中均持续优于最先进的单模态和多模态基线方法，尽管模型规模仅为最大基线方法的1/18。这些结果表明Echo2ECG是一种鲁棒且强大的ECG特征提取器。我们的代码访问地址为：https://github.com/michelleespranita/Echo2ECG。