Contact: Patrick Schwab, ETH Zurich patrick.schwab@hest.ethz.ch
Authors: See AUTHORS.txt
License: GPLv3; See LICENSE.txt
Description: Predicts the rhythm of given ECG signals using ensembles of recurrent neural networks. We delineated our approach in this manuscript. This solution is an entry to the PhysioNet / CinC challenge 2017.
If you reference our methodology, code or results in your work, please consider citing:
@inproceedings{schwab2017beat,
title={Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks},
author={Schwab, Patrick and Scebba, Gaetano and Zhang, Jia and Delai, Marco and Karlen, Walter},
booktitle={Computing in Cardiology},
year={2017}
}
Requires:
- pip
- Keras >= 1.2.2
- Theano >= 0.8.2
- matplotlib >= 1.3.1
- pandas >= 0.18.0
- h5py >= 2.6.0
- scikit-learn == 0.17.1
- pywavelets == 0.2.2
- imbalanced_learn == 0.2.1
- pyhsmm == 0.1.7
To train models you need to download the PhysioNet 2017 challenge data.
To save bidirectional RNNs you need to additionally patch the version of Keras installed by pip using this patch.
To train HSMMs you need to patch the PyHSMM library in pyhsmm_states.py line 1071 to:
obs, offset = obs[:,state], offset