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

arXiv: https://arxiv.org/abs/2410.19877 · HTML: https://mlsanity.com/paper/2410.19877/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2024.

## Built on by (most cited first, 8 of 8)

- [From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining](https://mlsanity.com/paper/2506.21803/) (2025)
- [BenchECG and xECG: a benchmark and baseline for ECG foundation models](https://mlsanity.com/paper/2509.10151/) (2025)
- [EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding](https://mlsanity.com/paper/2508.18785/) (2025)
- [ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine-tuning](https://mlsanity.com/paper/2503.13570/) (2025)
- [FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis](https://mlsanity.com/paper/2509.08961/) (2025)
- [SignalMC-MED: A Multimodal Benchmark for Evaluating Biosignal Foundation Models on Single-Lead ECG and PPG](https://mlsanity.com/paper/2603.09940/) (2026)
- [Position: Evaluation of ECG Representations Must Be Fixed](https://mlsanity.com/paper/2602.17531/) (2026)
- [CogAdapt: Adapting Clinical ECG Foundation Models for Wearable Cognitive Load Assessment](https://mlsanity.com/paper/2605.22774/) (2026)

## Abstract (arXiv)

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.