ankur219/ECG-Arrhythmia-classification
ECG arrhythmia classification using a 2-D convolutional neural network
This helps medical professionals or researchers automatically categorize ECG readings. It takes raw, one-dimensional ECG signals, transforms them into 2-D images, and then classifies them into one of seven categories, including normal and six types of arrhythmia. This tool is for clinicians or diagnostic technicians who need to quickly identify heart rhythm abnormalities from ECG data.
332 stars. No commits in the last 6 months.
Use this if you need an automated system to classify ECG signals into normal or specific arrhythmia types without manual feature extraction.
Not ideal if you require detailed, interpretative diagnostic reports beyond simple classification or if your ECG data is not suitable for conversion into 2-D image formats.
Stars
332
Forks
113
Language
Python
License
—
Category
Last pushed
Jan 28, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ankur219/ECG-Arrhythmia-classification"
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....