metadata
configs:
- config_name: default
data_files:
- path: data/train-\*
split: train
- path: data/val-\*
split: val
dataset_info:
dataset_size: 141628052928
download_size: 75032904415
features:
- dtype: string
name: id
- dtype: binary
name: dat
- dtype: string
name: hea
- dtype: image
name: mask
- dtype: image
name: img
- dtype: binary
name: dat_T0
- dtype: string
name: hea_T0
- dtype: image
name: mask_T0
- dtype: image
name: img_T0
splits:
- name: train
num_bytes: 134493058267
num_examples: 4112
- name: val
num_bytes: 7134994661
num_examples: 217
license: apache-2.0
Info
The dataset was generated using this fork of ECG-Image-Kit
https://github.com/Ahus-AIM/ecg-image-kit
and was used to train the segmentation network in
https://github.com/Ahus-AIM/Open-ECG-Digitizer
Download the dataset
from datasets import load_dataset
ds = load_dataset("Ahus-AIM/Open-ECG-Digitizer-Development-Dataset")
Mandatory citation
If you use this dataset, please cite
@article{stenhede_digitizing_2026,
title = {Digitizing Paper {ECGs} at Scale: An Open-Source Algorithm for Clinical Research},
author = {Stenhede, Elias and Bjørnstad, Agnar Martin and Ranjbar, Arian},
journal = {npj Digital Medicine},
year = {2026},
doi = {10.1038/s41746-025-02327-1},
url = {https://doi.org/10.1038/s41746-025-02327-1},
shorttitle = {Digitizing Paper {ECGs} at Scale}
}
@article{Shivashankara2024ECGImageKit,
title = {ECG-image-kit: A synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization},
author = {Shivashankara, Kshama Kodthalu and
Deepanshi and
Shervedani, Afagh Mehri and
Reyna, Matthew A. and
Clifford, Gari D. and
Sameni, Reza},
journal = {Physiological Measurement},
year = {2024},
publisher = {IOP Publishing},
doi = {10.1088/1361-6579/ad4954}
}