Kavli Affiliate: Xiang Zhang
| First 5 Authors: Yu Han, Yu Han, , ,
| Summary:
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.
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