landajuela/cardiac_challenge
Code repository for machine learning for cardiac electrophysiology
This project helps medical researchers and cardiologists better understand heart conditions by applying machine learning to electrocardiogram (ECG) data. It takes standard 12-lead ECG signals as input and can classify heartbeats as healthy or irregular, diagnose specific irregular heartbeats, and reconstruct detailed electrical activation maps of the heart. The primary users are researchers in cardiac electrophysiology or medical professionals interested in advanced ECG analysis.
No commits in the last 6 months.
Use this if you need to develop or experiment with machine learning models for analyzing ECG signals to detect heart conditions or reconstruct cardiac electrical activity.
Not ideal if you require a pre-built, production-ready diagnostic tool for immediate clinical use without custom model development.
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27
Forks
42
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Oct 08, 2025
Commits (30d)
0
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