--- 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\ and was used to train the segmentation network in\ # Download the dataset ``` python from datasets import load_dataset ds = load_dataset("Ahus-AIM/Open-ECG-Digitizer-Development-Dataset") ``` # Mandatory citation If you use this dataset, please cite ``` bibtex @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} } ```