ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine-tuning
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
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 .
