simonsanvil/ECG-classification-MLH
Diagnose types of Arrhythmia from ECG signals using Machine Learning and Deep Learning models.
This tool helps medical professionals quickly identify different types of Arrhythmia from an electrocardiogram (ECG) signal. You upload an ECG recording, and it tells you if a specific type of abnormal heart rhythm is present. This is designed for cardiologists, general practitioners, or medical students who need to interpret ECGs.
No commits in the last 6 months.
Use this if you need an automated initial assessment of an ECG to screen for various arrhythmia types.
Not ideal if you require a definitive, diagnostic-grade interpretation that replaces a human expert's clinical judgment.
Stars
18
Forks
6
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Oct 04, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/simonsanvil/ECG-classification-MLH"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
DeepPSP/torch_ecg
Deep learning ECG models implemented using PyTorch
im-ethz/flirt
Are you ready to FLIRT with your wearable data?
Edoar-do/HuBERT-ECG
A self-supervised foundation ECG model for broad and scalable cardiac applications
bowang-lab/ecg-fm
An electrocardiogram analysis foundation model.
antonior92/automatic-ecg-diagnosis
Scripts and modules for training and testing neural network for ECG automatic classification....