StefanHeng/ECG-Representation-Learning
Self-supervised pre-training for ECG representation with inspiration from transformers & computer vision
This project helps medical researchers and data scientists working with cardiac health data to interpret electrocardiogram (ECG) signals more effectively. It takes raw ECG recordings as input and produces learned representations of heart activity, which can then be used for tasks like disease classification or anomaly detection. The primary users are researchers focused on developing diagnostic tools for cardiovascular conditions.
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Use this if you need to extract meaningful, high-level features from large collections of raw ECG data to improve the accuracy of your diagnostic or analytical models.
Not ideal if you are looking for an out-of-the-box diagnostic tool for immediate clinical use, as this project focuses on pre-training representations rather than direct diagnosis.
Stars
29
Forks
7
Language
Python
License
MIT
Category
Last pushed
Sep 17, 2025
Commits (30d)
0
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