Link to Paper : https://ieeexplore.ieee.org/document/10364166
Results on local Validation
| AUPRC | AUROC | Accuracy | F-Measure | Challenge Score |
|---|---|---|---|---|
| 0.548 | 0.949 | 0.418 | 0.521 | 0.696 |
You can run this classifier code by installing the requirements
pip install requirements.txt
and running
python train_model.py training_data model
python test_model.py model test_data test_outputs
where training_data is a folder of training data files, model is a folder for saving your models, test_data is a folder of test data files (you can use the training data locally for debugging and cross-validation), and test_outputs is a folder for saving your models' outputs. The PhysioNet/CinC Challenge 2021 webpage provides training databases with data files and a description of the contents and structure of these files.
After training your model and obtaining test outputs with above commands, you can evaluate the scores of your models using the PhysioNet/CinC Challenge 2021 evaluation code by running
python evaluate_model.py labels outputs scores.csv class_scores.csv
where labels is a folder containing files with one or more labels for each ECG recording, such as the training database on the PhysioNet webpage; outputs is a folder containing files with outputs produced by your models for those recordings; scores.csv (optional) is a collection of scores for your models; and class_scores.csv (optional) is a collection of per-class scores for your models.
Docker and similar platforms allow you to containerize and package your code with specific dependencies that you can run reliably in other computing environments and operating systems.
To guarantee that we can run your code, please install Docker, build a Docker image from your code, and run it on the training data. To quickly check your code for bugs, you may want to run it on a subset of the training data.
If you have trouble running your code, then please try the follow steps to run the example code, which is known to work.
-
Create a folder
examplein your home directory with several subfolders.user@computer:~$ cd ~/ user@computer:~$ mkdir example user@computer:~$ cd example user@computer:~/example$ mkdir training_data test_data model test_outputs -
Download the training data from the Challenge website. Put some of the training data in
training_dataandtest_data. You can use some of the training data to check your code (and should perform cross-validation on the training data to evaluate your algorithm). -
Download or clone this repository in your terminal.
user@computer:~/example$ git clone https://github.com/physionetchallenges/python-classifier-2021.git -
Build a Docker image and run the example code in your terminal.
user@computer:~/example$ ls model python-classifier-2021 test_data test_outputs training_data user@computer:~/example$ ls training_data/ A0001.hea A0001.mat A0002.hea A0002.mat A0003.hea ... user@computer:~/example$ cd python-classifier-2021/ user@computer:~/example/python-classifier-2021$ docker build -t image . Sending build context to Docker daemon 30.21kB [...] Successfully tagged image:latest user@computer:~/example/python-classifier-2021$ docker run -it -v ~/example/model:/physionet/model -v ~/example/test_data:/physionet/test_data -v ~/example/test_outputs:/physionet/test_outputs -v ~/example/training_data:/physionet/training_data image bash root@[...]:/physionet# ls Dockerfile model test_data train_model.py extract_leads_wfdb.py README.md test_model.py helper_code.py requirements.txt test_outputs LICENSE team_code.py training_data root@[...]:/physionet# python train_model.py training_data model root@[...]:/physionet# python test_model.py model test_data test_outputs root@[...]:/physionet# exit Exit user@computer:~/example/python-classifier-2021$ cd .. user@computer:~/example$ ls test_outputs/ A0006.csv A0007.csv A0008.csv A0009.csv A0010.csv ...
Please see the PhysioNet/CinC Challenge 2021 webpage for more details. Please post questions and concerns on the Challenge discussion forum.