The George B. Moody PhysioNet Challenge

The George B. Moody PhysioNet Challenges are annual competitions that invite participants to develop automated approaches to address important physiological and clinical problems. The 2024 Challenge invites teams to develop algorithms for digitizing and classifying electrocardiograms (ECGs) captured from images or paper printouts. Despite the recent advances in digital ECG devices, physical or paper ECGs remain common, especially in the Global South. These physical ECGs document the history and diversity of cardiovascular diseases (CVDs), and algorithms that can digitize and classify these images have the potential to improve our understanding and treatment of CVDs, especially for underrepresented and underserved populations.

Learn more by visiting the challenge website.

Complimentary Software

MathWorks provides complimentary software for this competition. If your team is participating in this competition and needs software, fill out the software request form.

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Student Tutorials and Videos

Learn how to use MATLAB and Simulink to design algorithms, create simulations, deploy code, and speed up software development for your projects.

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Competition Resources

General Resources

Signal Processing in MATLAB

Machine Learning Challenges: Choosing the Best Classification Model and Avoiding Overfitting 

AI for Signals

Wavelet-based techniques for Signals and Images

What is Feature Extraction?

Examples

MATLAB Example Code for the PhysioNet Challenge 2024

Classify ECG Signals Using Long Short-Term Memory Networks

Signal Classification Using Wavelet-Based Features and Support Vector Machines

Train Convolutional Neural Network for Regression

Classify ECG Signals Using Long Short-Term Memory Networks 

Video Tutorials for Your Team

Interactive Online Tutorials

Learn by doing! Our two-hour online courses provide a hands-on learning environment where you will interact with a web-based version of MATLAB and receive instant and contextual feedback.

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