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  1. README.md +52 -27
README.md CHANGED
@@ -1,32 +1,33 @@
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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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- - split: train
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- path: data/train-*
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- - split: val
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- path: data/val-*
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  dataset_info:
 
 
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  features:
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- - name: id
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- dtype: string
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- - name: dat
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- dtype: binary
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- - name: hea
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- dtype: string
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- - name: mask
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- dtype: image
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- - name: img
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- dtype: image
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- - name: dat_T0
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- dtype: binary
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- - name: hea_T0
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- dtype: string
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- - name: mask_T0
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- dtype: image
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- - name: img_T0
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- dtype: image
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  splits:
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  - name: train
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  num_bytes: 134493058267
@@ -34,21 +35,30 @@ dataset_info:
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  - name: val
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  num_bytes: 7134994661
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  num_examples: 217
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- download_size: 75032904415
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- dataset_size: 141628052928
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  ---
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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},
@@ -58,3 +68,18 @@ If you use this dataset, please cite
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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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+
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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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+
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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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+
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  # Download the dataset
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+
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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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+
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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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+ @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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+ ```