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An official PyTorch implementation of 'Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms'

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Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiogram

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:

test_img

Requirements

  • Python 2.7
  • NumPy 1.16.1 (or later)
  • PyTorch 0.4.1 (or later)
  • Matplotlib
  • wfdb

Train the network

A reasonable set of hyperparameters is provided in train.sh. To train your own model:

mkdir results
./train.sh

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An official PyTorch implementation of 'Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms'

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