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 PhysioNet Challenge 2023 invites participants to predict outcomes for comatose patients in the hours following resuscitation from cardiac arrest. We ask participants to design and implement working, open-source algorithms that, based only on the provided electroencephalogram (EEG) recordings and routine patient data, can predict good and poor outcomes for patients. The team with the best score for this task on the hidden test set wins the Challenge.

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 

9 MATLAB Cheat Sheets for Data Science and Machine Learning

MATLAB Example Code for the PhysioNet Challenge 2023

Time-Frequency Convolutional Network for EEG Data Classification

Denoise EEG Signals Using Deep Learning Regression with GPU Acceleration

Time-Frequency Feature Embedding with Deep Metric Learning

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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