This repository contains a simple example that illustrates how to format a MATLAB entry for the George B. Moody PhysioNet Challenge 2025. If you are participating in the 2025 Challenge, then we recommend using this repository as a template for your entry. You can remove some of the code, reuse other code, and add new code to create your entry. You do not need to use the models, features, and/or libraries in this example for your entry. We encourage a diversity of approaches for the Challenges.
For this example, we implemented a Random Forest model with several simple features. (This simple example is not designed to perform well, so you should not use it as a baseline for your approach's performance.) You can try it by running the following commands on the Challenge training set. If you are using a relatively recent personal computer, then you should be able to run these commands from start to finish on a small subset of the training data in less than 30 minutes.
First, you can download and create data for these scripts by following the instructions in the "How do I create data for these scripts?" section in the Python example code repository.
Second, you must install the WFDB dependencies for these scripts by following the instructions on this page.
You can train your model(s) by running
train_model(training_data, model)
where
training_data(input; required) is a folder with the training data files, which must include the labels; andmodel(output; required) is a folder for saving your model.
You can run your trained model(s) by running
run_model(holdout_data, model, holdout_outputs)
where
holdout_data(input; required) is a folder with the holdout data files, which will not necessarily include the labels;model(input; required) is a folder for loading your model; andholdout_outputs(output; required) is a folder for saving your model outputs.
The Challenge website provides a training database with a description of the contents and structure of the data files.
You can evaluate your model by pulling or downloading the evaluation code and running
evaluate_model(labels, holdout_outputs, scores.csv)
where
labels(input; required) is a folder with labels for the holdout data files, which must include the labels;holdout_outputs(input; required) is a folder containing files with your model's outputs for the data; andscores.csv(output; optional) is file with a collection of scores for your model.
You can use the provided training set for the training_data and holdout_data files, but we will use different datasets for the validation and test sets, and we will not provide the labels to your code.
You can use the scripts in this repository convert CODE-15% dataset to WFDB format. The instructions use code15_hdf5 as the path for the input data files and code15_wfdb for the output data files, but you can replace them with the absolute or relative paths for the files on your machine.
-
Download and unzip one or more of the
exam_partfiles and theexams.csvfile in the CODE-15% dataset. -
Download and unzip the Chagas labels, i.e., the
code15_chagas_labels.csvfile. -
Convert the CODE-15% dataset to WFDB format, with the available demographics information and Chagas labels in the WFDB header file, by running
python prepare_code15_data.py \ -i code15_hdf5/exams_part0.hdf5 code15_hdf5/exams_part1.hdf5 \ -d code15_hdf5/exams.csv \ -l code15_hdf5/code15_chagas_labels.csv \ -o code15_wfdb
Each exam_part file in the CODE-15% dataset contains approximately 20,000 ECG recordings. You can include more or fewer of these files to increase or decrease the number of ECG recordings, respectively. You may want to start with fewer ECG recordings to debug your code.
Additionaly, you can add the -f mat argument to convert to .mat files instead of .dat files. However, we will use WFDB format with .dat files to run your submitted code.
Please edit the following script to add your code:
team_train_model.mis a script for training your model.load_model.mis a script for loading your trained model.team_run_model.mis a script for running your trained model.
Please do not edit the following scripts. We will use the unedited versions of these scripts when running your code:
train_model.mis a script for training your model.run_model.mis a script for running your trained model.
These scripts must remain in the root path of your repository, but you can put other scripts and other files elsewhere in your repository.
To train and save your model, please edit the team_train_model.m script. Please do not edit the input or output arguments of this function.
To load and run your trained model, please edit the load_model.m and team_run_model.m scripts. Please do not edit the input or output arguments of these functions.
This repository does not include code for evaluating your entry. Please see the evaluation code repository for code and instructions for evaluating your entry using the Challenge scoring metric.
Please see the Challenge website for more details. Please post questions and concerns on the Challenge discussion forum. Please do not make pull requests, which may share information about your approach.