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## ECG classification from single-lead segments using _Deep Convolutional Neural Networks_ and _Feature-Based Approaches_

## 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

#### 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): 

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).

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:

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 |

| Class  | Description |

| ----- | -------------------:|

| ----- | -------------------:|

| N | normal sinus rhythm |

| N | normal sinus rhythm |

| A | atrial fibrillation (AF) |

| A | atrial fibrillation (AF) |

| O | other cardiac rhythms |

| O | other cardiac rhythms |

| ~ | noise segment |

| ~ | noise segment |

Two methodologies are proposed and described in distict forlder within this repo:

Two methodologies are proposed and described in distict forlder within this repo:

* Classic feature-based MATLAB approach (`featurebased-approach` folder)

* Classic feature-based MATLAB approach (`featurebased-approach` folder)

* Deep Convolutional Network Approach in Python (`deeplearn-approach` folder)

* Deep Convolutional Network Approach in Python (`deeplearn-approach` folder)

## Downloading Challenge data

## Downloading Challenge data

For downloading the [challenge training set](https://physionet.org/challenge/2017/training2017.zip). This can be done on Linux using:

For downloading the [challenge training set](https://physionet.org/challenge/2017/training2017.zip). This can be done on Linux using:

```bash

```bash

wget https://physionet.org/challenge/2017/training2017.zip

wget https://physionet.org/challenge/2017/training2017.zip

unzip training2017.zip

unzip training2017.zip

```

```

## Acknowledgment

## Acknowledgment

All authors are affilated at the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford.

All authors are affilated at the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford.

## License

## License

Released under the GNU General Public License v3

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 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.

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/.

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

## References

When using this code, please cite [1].

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).

[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).

[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).