This repository includes a PyTorch implementation of 'Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms' which can be found here. Our code makes use of the Physikalisch-Technische Bundesanstalt (PTB) database, available here, and is based on the ConvNetQuake architecture, as described here.
The architecture of the model is as follows:
- Python 2.7
- NumPy 1.16.1 (or later)
- PyTorch 0.4.1 (or later)
- Matplotlib
- wfdb
A reasonable set of hyperparameters is provided in train.sh. To train your own model:
mkdir results
./train.sh