We implemented a linear regression model classifier that uses age, sex, and the root mean square of the ECG lead signals as features. This simple example illustrates how to format your MATLAB entry for the Challenge. However, it is not designed to score well (or, more accurately, designed not to do well), so you should not use it as a baseline for your model's performance.
The Matlab code contains two main sections:
- train: an example code to read the data and train a multiclass linear regression model.
- test: an example code to classify and make prediction based on your training model.
You can run this classifier code by starting MATLAB and running
train_model(training_data, model)
test_model(model, test_data, test_outputs)
where training_data is a directory of training data files, model is a directory for saving your model, test_data is the directory of test data files (you can use the training data locally for debugging and cross-validation), and test_outputs is a directory for saving your model's outputs. The PhysioNet/CinC Challenge 2021 webpage provides a training database with data files and a description of the contents and structure of these files.
After training your model and obtaining test outputs using the above two command lines, you can evaluate the scores of your model using the PhysioNet/CinC Challenge 2021 evaluation code by running
evaluate_model(labels, outputs, scores.csv, class_scores.csv)
where labels is a directory containing files with one or more labels for each ECG recording, such as the training database on the PhysioNet webpage; outputs is a directory containing files with outputs produced by your algorithm for those recordings; scores.csv (optional) is a collection of scores for your algorithm; and class_scores.csv (optional) is a collection of per-class scores for your algorithm.
We will run the train_model.m and test_model.m scripts to run your training code and testing code, so check these scripts and the functions that they call.
Our example code uses four main scripts to train and test your model for the 2021 Challenge:
Please edit the following scripts to add your training and testing code:
team_training_code.mis a script with functions for training your model and running your trained model. It also callsget_features.mfucntion that you can edit.team_testing_code.mis a script with functions for loading your model, extracting features from your test data and predict outputs using the classifier.
Please do not edit the following scripts. We will use the unedited versions of these scripts.
train_model.mis a script for calling your training code on the training data.test_model.mis a script for calling your trained model on the test data.
Please do not edit the following functions. We will use the unedited versions of these functions.
get_leads.mis a function for extracting the available leads (name and index).extract_data_from_header.mis a function for extracting the data information from the header files.
These scripts must remain in the root path of your repository, but you can put other scripts and other files in subfolders.
To train and save your model, please edit the team_training_code.m script and the get_features.m function in the team_training_code.m script. team_training_code.m is a script for training your model and get_features.m is a function for preprocessing the signals and extracting features from ECG leads. Please do not edit the input or output arguments of team_training_code.m script.
To load and run your trained model, please edit the team_testing_code.m, which takes an ECG recording as an input and returns the class labels and probabilities for the ECG recording as outputs. Please do not edit the input or output arguments of team_testing_code.m script for loading or running your models.
In the Python example code, we included a script, extract_leads_wfdb.py, for extracting reduced-lead sets from the training data. You can use this script to test your code locally.
Please see the PhysioNet/CinC Challenge 2021 webpage for more details. Please post questions and concerns on the Challenge discussion forum.