Example Python classifier for the PhysioNet/CinC Challenge 2017

Contents

This classifier uses three scripts:

• util.py contains two functions named load and save, which are used to load and save processing as a binary file.

• load.py contains the processing function of data before input to model.

• challenge.py contains load_ECG_model and run_ECG_classifier functions. It calls another two scripts. In challenge.py, load_ECG_model was called once to load model and run_ECG_classifier was called many times to predict for all files. This script output a answer.txt file to save the predict result.

Use

Here are instructions for testing the code in Linux.
First, create a folder, docker_test, in your home directory. Then, git clone the code in docker_test/cinc2017_test.

git clone https://github.com/stfeiseu/cinc2017.git

Finally, build a Docker image and run the example code using the following steps:

First, switch the current directory to the ~/docker_test/cinc2017_test.

Then, execute the following command to build a docker image.

sudo docker build -t image .

sudo docker run -it -v ~/docker_test/validation:/physionet/input_directory -v ~/docker_test/cinc2017_test:/physionet/output_directory image bash

Note: In the last command, the directory (~/docker_test/validation) followed by the first -v is the directory where the dataset is located on my computer, you can change ~/docker_test/validation to the directory of data set on your computer.

At last, you can run this classifier by running

python challenge.py input_directory output_directory

where input_directory is a directory for input data files and output_directory is a directory for output classification files.

After completing the above step, a answer.txt file will be generated in the folder ~/docker_test/cinc2017_test/output_directory.

The PhysioNet/CinC 2017 webpage (https://physionet.org/content/challenge-2017/1.0.0/) provides a training database with data files and a description of the contents and structure of these files.

How do I install Docker?
Go to https://docs.docker.com/install/ and install the Docker Community Edition. For troubleshooting, see https://docs.docker.com/config/daemon/

Reference: This file refers to https://physionetchallenges.github.io/2020/submissions#docker