An Electrocardiogram Foundation Model Built on over 10 Million Recordings.

ABSTRACT:

Background

Artificial intelligence (AI) has demonstrated significant potential in electrocardiogram (ECG) analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI, bringing benefits such as efficient disease diagnosis and cross-domain knowledge transfer. The development of an ECG foundation model holds the promise of elevating AI-ECG research to new heights. However, building such a model poses several challenges, including insufficient database sample sizes and inadequate generalization across multiple domains. In addition, there is a notable performance gap between single-lead and multilead ECG analysis.

Methods

We propose a general-purpose ECG foundation model (ECGFounder), which leverages real-world

SUBMITTER: Li J 

PROVIDER: S-EPMC12327759 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature