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