Figure 1: This plot is made by using ecg plot [1] and the ECG data is from the PTB Diagnostic DB [2].

This project is based on the work we did in the PhysioNet/Computing in Cardiology Challenge 2020. This paper [3] describes the Challenge and this paper discribes our contribution in this challenge.

The data set in this project contains 43.101 ECGs and comes from six different sources. Table 1 show the six sources.

Table 1: The table lists the six different sources used in the data set in this project

To get access to the data used in this study you can either download it from https://physionetchallenges.github.io/2020/#data or download the same data set from Kaggle. To use the codes in this repository you should sign up for a Kaggle account and get a Kaggle API token and use this to get access to the Kaggle data set from Google Colab. Google Colab Pro was used to get sufficient GPU power and enough runtime.

1. Log in to your Kaggle account or sign up here
2. On the left side of the "edit profile"-button you click on the "Account"-option.
3. Scroll down to the API-section and click "Create New API Token"-button.
4. You will now have a file named kaggle.json. This is your API-token
5. You can upload the kaggle.json-file to the Google Colab notebook and then you are able to download datasets from Kaggle

The results from the cross-validated models can be plotted with this notebook . The figures can be found here.

The paper describing the work in this project can be found here:

Licensed under the Apache 2.0 License

@inproceedings{singstad2020convolutional,
title={Convolutional neural network and rule-based algorithms for classifying 12-lead ecgs},
author={Singstad, Bj{\o}rn-Jostein and Tronstad, Christian},
booktitle={2020 Computing in Cardiology},
pages={1--4},
year={2020},
organization={IEEE}
}