Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos

TL;DR

Echo2ECG introduces a multimodal self-supervised framework that enhances ECG representations with cardiac morphology from multi-view echocardiography, improving clinical task performance while being computationally efficient.

Contribution

It is the first to integrate multi-view echocardiography data into ECG representation learning, addressing the representational mismatch of previous methods.

Findings

Outperforms state-of-the-art baselines on cardiac phenotype classification.

Enables retrieval of echocardiography studies with similar morphology using ECGs.

Uses 18x less model size than the largest baseline.

Abstract

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.