sucharitha1812/ecg-anomaly-detection-vae

Unsupervised ECG anomaly detection using Variational Autoencoder (VAE) with reconstruction error-based classification, achieving strong performance (AUC: 0.96) for healthcare time-series analysis.

14
/ 100
Experimental
No License No Package No Dependents
Maintenance 13 / 25
Adoption 0 / 25
Maturity 1 / 25
Community 0 / 25

How are scores calculated?

Stars

—

Forks

—

Language

Jupyter Notebook

License

—

Category

ecg-arrhythmia-diagnosis

Last pushed

Mar 23, 2026

Commits (30d)

0

GitHub
ECG Arrhythmia Diagnosis · 108 frameworks

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/sucharitha1812/ecg-anomaly-detection-vae"

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

64

im-ethz/flirt

Are you ready to FLIRT with your wearable data?

54

Edoar-do/HuBERT-ECG

A self-supervised foundation ECG model for broad and scalable cardiac applications

46

AmbiqAI/heartkit

Perform AI-based heart monitoring tasks

44

antonior92/automatic-ecg-diagnosis

Scripts and modules for training and testing neural network for ECG automatic classification....

44

Explore ML Frameworks

All categories Trending ML Framework directory Insights