https://github.com/fernandoandreotti/cinc-challenge2017
ECG classification from short single lead segments (Computing in Cardiology Challenge 2017 entry)
https://github.com/fernandoandreotti/cinc-challenge2017
arrhythmia cardiology challenge classification convolutional-neural-networks deep-convolutional-networks ecg physionet
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ECG classification from short single lead segments (Computing in Cardiology Challenge 2017 entry)
- Host: GitHub
- URL: https://github.com/fernandoandreotti/cinc-challenge2017
- Owner: fernandoandreotti
- License: gpl-3.0
- Created: 2017-09-14T09:26:10.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-11-07T20:25:10.000Z (over 6 years ago)
- Last Synced: 2024-02-01T08:51:58.492Z (over 2 years ago)
- Topics: arrhythmia, cardiology, challenge, classification, convolutional-neural-networks, deep-convolutional-networks, ecg, physionet
- Language: MATLAB
- Homepage:
- Size: 20 MB
- Stars: 146
- Watchers: 12
- Forks: 70
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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## ECG classification from single-lead segments using _Deep Convolutional Neural Networks_ and _Feature-Based Approaches_
#### Our entry for the Computing in Cardiology Challenge 2017: Atrial Fibrillation (AF) Classification from a short single lead Electrocardiogram (ECG) recording
When using this code, please cite [our paper](http://prucka.com/2017CinC/pdf/360-239.pdf):
> Andreotti, F., Carr, O., Pimentel, M.A.F., Mahdi, A., & De Vos, M. (2017). Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG. In Computing in Cardiology. Rennes (France).
This repository contains our solution [1] to the Physionet Challenge 2017 presented at the Computing in Cardiology conference 2017. As part of the Challenge, based on short single-lead ECG segments with 10-60 seconds duration, the classifier should output one of the following classes:
| Class | Description |
| ----- | -------------------:|
| N | normal sinus rhythm |
| A | atrial fibrillation (AF) |
| O | other cardiac rhythms |
| ~ | noise segment |
Two methodologies are proposed and described in distict forlder within this repo:
* Classic feature-based MATLAB approach (`featurebased-approach` folder)
* Deep Convolutional Network Approach in Python (`deeplearn-approach` folder)
## Downloading Challenge data
For downloading the [challenge training set](https://physionet.org/challenge/2017/training2017.zip). This can be done on Linux using:
```bash
wget https://physionet.org/challenge/2017/training2017.zip
unzip training2017.zip
```
## Acknowledgment
All authors are affilated at the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford.
## License
Released under the GNU General Public License v3
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
## References
When using this code, please cite [1].
[1]: Andreotti, F., Carr, O., Pimentel, M.A.F., Mahdi, A., & De Vos, M. (2017). Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG. In Computing in Cardiology. Rennes (France).
[2]: Clifford, G.D., Liu, C., Moody, B., Silva, I., Li, Q., Johnson, A.E.W., & Mark, R.G. (2017). AF Classification from a Short Single Lead ECG Recording: the PhysioNet Computing in Cardiology Challenge 2017. In Computing in Cardiology. Rennes (France).