{"repo":"physhik/ecg-mit-bih","free":true,"listed":false,"github":"https://github.com/physhik/ecg-mit-bih","clone":"git clone https://github.com/physhik/ecg-mit-bih.git","description":"ECG classification using MIT-BIH data, a deep CNN learning implementation of Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network, https://www.nature.com/articles/s41591-018-0268-3 and also deploy the trained model to a web app using Flask, introduced at","language":"Python","stars":268,"topics":["cardiology","ecg","cinc","flask","cnn","mit-bih-arrhythmia"],"license":"GPL-3.0","category":"machine-learning","readme_excerpt":"ECG classification using MIT-BIH dataset This repo is an implementation of https://www.nature.com/articles/s41591-018-0268-3 and https://arxiv.org/abs/1707.01836 and focus on training using a MIT-BIH dataset. If you want to train using CINC or open irhythm data, see the open source which the authors of the original research paper have coded at https://github.com/awni/ecg Introduction to MIT-BIH dataset at physionet : https://physionet.org/physiobank/database/mitdb/ Dependency (Updated, April 1, 2025) - Python == 3.12.9 - Flask==3.1.0 - gevent==24.11.1 - keras==3.9.1 - numpy==2.1.3 - pip-tools==7.4.1 - scikit-learn==1.6.1 - scipy==1.15.2 - six==1.17.0 - tensorflow==2.19.0 - tensorflow-metal==1.2.0 - tqdm==4.67.1 - Werkzeug==3.1.3 - wfdb==4.2.0 Data setup and train I recommend using a vitual enviroment for Python, so run setup.sh in order to install and to activate it. Now you have a trained model for ECG classification Test Predict an annotation of CINC2017 data or your own data(csv file) It randomly chooses one of data, and predict the slices of the signal. Run predict.py in the virtual environment we have already set up. --cinc download branch is used at first to download the CINC2017 data. See src/config.py and customize your parameters or learn better way to train and test Jupyter notebook example In case, you do not have a GPU above a decent performance, you might be able to use Google colab. Follow the Jupyter notebook. Flask web app The flask web app is based on the ker","default_branch":null,"files":null,"tree":[],"storefront":"/r/physhik","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/physhik/ecg-mit-bih/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}