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--- a/README.md +++ b/README.md @@ -1,58 +1,58 @@ -[](./LICENSE) -[](https://paperswithcode.com/sota/arrhythmia-detection-on-the-physionet?p=comparing-feature-based-classifiers-and) - -## 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). +[](./LICENSE) +[](https://paperswithcode.com/sota/arrhythmia-detection-on-the-physionet?p=comparing-feature-based-classifiers-and) + +## 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).