A Systematic Review on Foundation Models for Electrocardiogram Analysis: Initial Strides and Expansive Horizons

Abstract

Electrocardiogram (ECG) is widely used in healthcare applications, such as arrhythmia detection and sleep monitoring, making accurate ECG analysis critically essential. Traditional deep learning models for ECG are task-specific, with limited generalization and narrow functionality. Foundation models (FMs), or large pre-training models, have recently advanced representation learning, enabling strong performance across diverse tasks and motivating their adoption for ECG analysis. Here, we present the first comprehensive review dedicated to ECG foundation models (ECG-FMs). We map the current landscape of architectures, pre-training paradigms, and adaptation strategies, and critically examine their strengths, limitations, and clinical potential. By consolidating this emerging field, we aim to accelerate the development of robust, generalizable ECG-FMs and chart future directions for their integration into healthcare practice.