Kavli Affiliate: Xiang Zhang
| First 5 Authors: Yu Han, Xiaofeng Liu, Xiang Zhang, Cheng Ding,
| Summary:
The electrocardiogram (ECG) is ubiquitous across various healthcare domains,
such as cardiac arrhythmia detection and sleep monitoring, making ECG analysis
critically essential. Traditional deep learning models for ECG are
task-specific, with a narrow scope of functionality and limited generalization
capabilities. Recently, foundation models (FMs), also known as large
pre-training models, have fundamentally reshaped the scheme of model design and
representation learning, enhancing the performance across a variety of
downstream tasks. This success has drawn interest in the exploration of FMs to
address ECG-based medical challenges concurrently. This survey provides a
timely, comprehensive and up-to-date overview of FMs for large-scale ECG-FMs.
First, we offer a brief background introduction to FMs. Then, we discuss the
model architectures, pre-training methods, and adaptation approaches of ECG-FMs
from a methodology perspective. Despite the promising opportunities of ECG-FMs,
we also outline the challenges and potential future directions. Overall, this
survey aims to provide researchers and practitioners with insights into the
research of ECG-FMs on theoretical underpinnings, domain-specific applications,
and avenues for future exploration.
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