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The official repository for the paper Benchmarking ECG FMs: A Reality Check Across Clinical Tasks
🎉 Our paper has been accepted at the Fourteenth International Conference on Learning Representations (ICLR 2026). OpenReview: https://openreview.net/forum?id=xXRqWpt3Xr
Here, we benchmark ECG foundation models across 12 public datasets and 26 clinically relevant tasks encompassing 1,650 regression and classification targets. We also proposed ECG-CPC, a new and outperforming ECG foundational model. We provide scripts, configurations, and checkpoints to evaluate models efficiently and reproducibly.
You can download the datasets from the following sources:
Label mappings 2021.xlsx in the respective dataset folder. The original file is linked on the CinC21 challenge website. )Download pretrained checkpoints for evaluation:
Set up the Python environment using the provided YAML files:
# General environment
conda env create -f env.yaml
# For ECG-FM evaluation
conda env create -f ecg_fm_env.yaml
Follow these steps to set up and run the benchmark:
First, preprocess all datasets using the provided preprocess_ecg_dataset.ipynb Jupyter notebook.
Before running the benchmark, configure the necessary paths:
run.sh fileOpen run.sh in your preferred text editor and update the following variables with your local paths:
# Set these paths according to your system
BASE_DIR="/path/to/your/fm-benchmarking"
CHECKPOINTS_DIR="/path/to/your/checkpoints"
DATASET_DIR="/path/to/your/datasets"
Modify the dataset path in the fm-benchmarking/code/clinical_ts/models/conf/data/ecg_ptbxl.yaml
run.sh filesbatch run.sh
@inproceedings{
al-masud2026benchmarking,
title={Benchmarking {ECG} Foundational Models: A Reality Check Across Clinical Tasks},
author={M A Al-Masud and Juan Lopez Alcaraz and Nils Strodthoff},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=xXRqWpt3Xr}
}
$ claude mcp add ecg-fm-benchmarking \
-- python -m otcore.mcp_server <graph>