ECG-Image-Kit

A toolkit for synthesis, analysis, and digitization of electrocardiogram images

Citation

Please include references to the following articles in any publications:

1. Kshama Kodthalu Shivashankara, Deepanshi, Afagh Mehri Shervedani, Matthew A. Reyna, Gari D. Clifford, Reza Sameni (2024). ECG-image-kit: a synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization. In Physiological Measurement. IOP Publishing. doi: 10.1088/1361-6579/ad4954

2. ECG-Image-Kit: A Toolkit for Synthesis, Analysis, and Digitization of Electrocardiogram Images, (2024). URL: https://github.com/alphanumericslab/ecg-image-kit

Contributors

• Elias Stenhede, Department of Medical Technology and E-health, Akershus University Hospital, Norway
• Agnar Martin Bjørnstad, Department of Medical Technology and E-health, Akershus University Hospital, Norway
• Deepanshi, Department of Biomedical Informatics, Emory University, GA, US
• Kshama Kodthalu Shivashankara, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, US
• Matthew A Reyna, Department of Biomedical Informatics, Emory University, GA, US
• Gari D Clifford, Department of Biomedical Informatics and Biomedical Engineering, Emory University and Georgia Tech, GA, US
• Reza Sameni (contact person), Department of Biomedical Informatics and Biomedical Engineering, Emory University and Georgia Tech, GA, US

Installation

• Clone this repository and move to the root folder of the repository.
• Create a venv with python3.12:

  python3.12 -m venv venv
  

• Activate the venv:

  source venv/bin/activate
  

• Install the required packages:

  python3 -m pip install -r requirements.txt
  

ECG Data

A few samples from the PTB-XL dataset are already stored in data. If you with to generate a diverse dataset, you should download the full dataset. If you use the examples or the full dataset, please cite:

1. Wagner, P., Strodthoff, N., Bousseljot, R., Samek, W., & Schaeffter, T. (2022). PTB-XL, a large publicly available electrocardiography dataset (version 1.0.3). PhysioNet. doi: 10.13026/kfzx-aw45

Run the pipeline

1. Take a look in the config file
2. Run the pipeline with the following command:

   python src/generate_images.py