elenamer/ecg_classification_DL
ECGDL: A framework for comparative study of databases and computational methods for arrhythmia detection from single-lead ECG
This tool helps medical researchers and data scientists compare various methods for detecting heart arrhythmias from single-lead ECG data. It standardizes different ECG datasets and classification algorithms, allowing you to input raw ECG recordings and analyze their performance across various arrhythmia detection tasks. It's designed for those evaluating or developing new arrhythmia detection models.
No commits in the last 6 months.
Use this if you need to systematically compare different computational methods and ECG datasets for arrhythmia detection using single-lead ECGs.
Not ideal if you are a clinician looking for a diagnostic tool or a patient seeking medical advice; this is a research and development framework.
Stars
19
Forks
1
Language
Python
License
—
Category
Last pushed
Aug 31, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/elenamer/ecg_classification_DL"
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....