IntroECG GitHub

A full-process library for deep learning on 12-lead electrocardiograms

Click here to go to the repo

1-Waveform Extraction

Scripts and tutorial for extracting raw ECG waveforms from GE Muse or PDFs of ECGs. It also includes examples of how to display and review your ECG data.

2-Generating Synthetic ECG Data

Generate your own synthetic electrocardiograms. Comes with the ability to alter many different aspects of the waveform to test different hypotheses.


Key preprocessing steps for cleaning and normalizing ECG data.


Different example models we've built to showcase approaches that work for electrocardiograms, in pytorch and tensorflow/keras.

5-Training with Ignite and Optuna

A framework built on PyTorch Ignite using Optuna to allow for rapid experimentation and displaying your results using Tensorboard

6-Putting it into Practice: The ValveNet Model

See the notebooks we used for generating our figures and key results on our valvular heart disease model published in JACC.

Development Team

Lead Developers:
-Pierre Elias
-Adler Perotte

-Vijay Rajaram
-Shengqing Xia
-Alex Wan
-Junyang Jiang
-Yuge Shen
-Han Wang