ridwanalam / introecg

Resource library for getting started with deep learning work using electrocardiograms

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

This repository is meant to be a useful resource library for getting started with deep learning work using electrocardiograms.

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.

3-Preprocessing

Key preprocessing steps for cleaning and normalizing ECG data.

4-Models

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

Development Team

Lead Developers:
-Pierre Elias
-Adler Perotte

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