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
Data set number | Name |
1 | China Physiological Signal Challenge 2018 |
2 | China Physiological Signal Challenge 2018 Extra |
3 | St.Petersburg Institute of Cardiological Technics |
4 | PTB Diagnostics |
5 | PTB-XL |
6 | Georgia 12-Lead ECG Challenge Database |
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.
- Log in to your Kaggle account or sign up here
- On the left side of the "edit profile"-button you click on the "Account"-option.
- Scroll down to the API-section and click "Create New API Token"-button.
- You will now have a file named kaggle.json. This is your API-token
- You can upload the kaggle.json-file to the Google Colab notebook and then you are able to download datasets from Kaggle
Model number | Model | Link to Google Colab Notebook | Link to Notebook on github |
1 | FCN | Notebook | |
2 | Encoder | Notebook | |
3 | FCN + MLP | Notebook | |
4 | Encoder + MLP | Notebook | |
5 & 6 | Encoder + FCN (and Encoder + FCN + rule-based model) | Notebook | |
7 & 8 | Encoder + FCN + MLP + (and Endcoder + FCN + MLP + Rule-based model) | Notebook |
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
[1] | ECG plot: https://github.com/dy1901/ecg_plot |
[2] | PTB Diagnostic DB: Bousseljot R, Kreiseler D, Schnabel, A. Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet. Biomedizinische Technik, Band 40, Ergänzungsband 1 (1995) S 317 (https://physionet.org/content/ptbdb/1.0.0/) |
[3] | Perez Alday, Erick A, Annie Gu, Amit J Shah, Chad Robichaux, An-Kwok Ian Wong, Chengyu Liu, Feifei Liu, mfl. «Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020». Physiological Measurement, 11. november 2020. https://doi.org/10.1088/1361-6579/abc960. |