# ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine-tuning

arXiv: https://arxiv.org/abs/2503.13570 · HTML: https://mlsanity.com/paper/2503.13570/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 70 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [ECG-FM: An Open Electrocardiogram Foundation Model](https://mlsanity.com/paper/2408.05178/) (2024)
- [Improving Position Encoding of Transformers for Multivariate Time Series Classification](https://mlsanity.com/paper/2305.16642/) (2023)
- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains](https://mlsanity.com/paper/2410.04133/) (2024)
- [Reproducibility in Machine Learning for Health](https://mlsanity.com/paper/1907.01463/) (2019)
- [Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata](https://mlsanity.com/paper/2308.15291/) (2023)
- [A Systematic Review on Foundation Models for Electrocardiogram Analysis: Initial Strides and Expansive Horizons](https://mlsanity.com/paper/2410.19877/) (2024)
- [AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings](https://mlsanity.com/paper/2411.17711/) (2024)
- [Deep Neural Networks Generalization and Fine-Tuning for 12-lead ECG Classification](https://mlsanity.com/paper/2305.18592/) (2023)
- [The Rlign Algorithm for Enhanced Electrocardiogram Analysis through R-Peak Alignment for Explainable Classification and Clustering](https://mlsanity.com/paper/2407.15555/) (2024)

## Built on by (most cited first, 1 of 1)

- [FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis](https://mlsanity.com/paper/2509.08961/) (2025)

## Abstract (arXiv)

Electrocardiogram data, one of the most widely available biosignal data, has become increasingly valuable with the emergence of deep learning methods, providing novel insights into cardiovascular diseases and broader health conditions. However, heterogeneity of electrocardiogram formats, limited access to deep learning model weights and intricate algorithmic steps for effective fine-tuning for own disease target labels result in complex workflows. In this work, we introduce ExChanGeAI, a web-based end-to-end platform that streamlines the reading of different formats, pre-processing, visualization and custom machine learning with local and privacy-preserving fine-tuning. ExChanGeAI is adaptable for use on both personal computers and scalable to high performance server environments. The platform offers state-of-the-art deep learning models for training from scratch, alongside our novel open-source electrocardiogram foundation model CardX, pre-trained on over one million electrocardiograms. Evaluation across three external validation sets, including an entirely new testset extracted from routine care, demonstrate the fine-tuning capabilities of ExChanGeAI. CardX outperformed the benchmark foundation model while requiring significantly fewer parameters and lower computational resources. The platform enables users to empirically determine the most suitable model for their specific tasks based on systematic validations.The code is available at https://imigitlab.uni-muenster.de/published/exchangeai .