文件列表:

LICENSE (35147, 2019-01-15)
ecg (0, 2019-01-15)
ecg\__init__.py (0, 2019-01-15)
ecg\load.py (2764, 2019-01-15)
ecg\network.py (4349, 2019-01-15)
ecg\predict.py (673, 2019-01-15)
ecg\train.py (2873, 2019-01-15)
ecg\util.py (354, 2019-01-15)
examples (0, 2019-01-15)
examples\cinc17 (0, 2019-01-15)
examples\cinc17\build_datasets.py (1352, 2019-01-15)
examples\cinc17\config.json (477, 2019-01-15)
examples\cinc17\entry (0, 2019-01-15)
examples\cinc17\entry\AUTHORS.txt (33, 2019-01-15)
examples\cinc17\entry\LICENSE.txt (35147, 2019-01-15)
examples\cinc17\entry\dependencies.txt (303, 2019-01-15)
examples\cinc17\entry\evaler.py (735, 2019-01-15)
examples\cinc17\entry\next.sh (829, 2019-01-15)
examples\cinc17\entry\prepare-entry.sh (822, 2019-01-15)
examples\cinc17\entry\requirements.txt (487, 2019-01-15)
examples\cinc17\entry\setup.sh (1867, 2019-01-15)
examples\cinc17\entry\weights_only.py (118, 2019-01-15)
examples\cinc17\notebooks (0, 2019-01-15)
examples\cinc17\notebooks\cinc17_eval.ipynb (4094, 2019-01-15)
examples\cinc17\setup.sh (248, 2019-01-15)
examples\irhythm (0, 2019-01-15)
examples\irhythm\build_datasets.py (4904, 2019-01-15)
examples\irhythm\config.json (455, 2019-01-15)
examples\irhythm\notebooks (0, 2019-01-15)
examples\irhythm\notebooks\agreement_rates.ipynb (5959, 2019-01-15)
examples\irhythm\notebooks\cinc17_eval.ipynb (71267, 2019-01-15)
examples\irhythm\notebooks\data_analysis.ipynb (2433, 2019-01-15)
examples\irhythm\notebooks\dev_results.ipynb (9224, 2019-01-15)
... ...

## Install Clone the repository ``` git clone git@github.com:awni/ecg.git ``` If you don't have `virtualenv`, install it with ``` pip install virtualenv ``` Make and activate a new Python 2.7 environment ``` virtualenv -p python2.7 ecg_env source ecg_env/bin/activate ``` Install the requirements (this may take a few minutes). For CPU only support run ``` ./setup.sh ``` To install with GPU support run ``` env TF=gpu ./setup.sh ``` ## Training In the repo root direcotry (`ecg`) make a new directory called `saved`. ``` mkdir saved ``` To train a model use the following command, replacing `path_to_config.json` with an actual config: ``` python ecg/train.py path_to_config.json ``` Note that after each epoch the model is saved in `ecg/saved///.hdf5`. For an actual example of how to run this code on a real dataset, you can follow the instructions in the cinc17 [README](examples/cinc17/README.md). This will walk through downloading the Physionet 2017 challenge dataset and training and evaluating a model. ## Testing After training the model for a few epochs, you can make predictions with. ``` python ecg/predict.py .json .hdf5 ``` replacing `` with an actual path to the dataset and `` with the path to the model. ## Citation and Reference This work is published in the following paper in *Nature Medicine* [Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network](https://www.nature.com/articles/s41591-018-0268-3) If you find this codebase useful for your research please cite: ``` @article{hannun2019cardiologist, title={Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network}, author={Hannun, Awni Y and Rajpurkar, Pranav and Haghpanahi, Masoumeh and Tison, Geoffrey H and Bourn, Codie and Turakhia, Mintu P and Ng, Andrew Y}, journal={Nature Medicine}, volume={25}, number={1}, pages={65}, year={2019}, publisher={Nature Publishing Group} } ```

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：