Ecg Mit Bih
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 us...
Overview
This product is an implementation of ECG classification using the MIT-BIH dataset. It focuses on training using a MIT-BIH dataset and provides instructions for training, testing, and using the model for ECG classification. The product includes dependencies, setup instructions, and options for trying out the model on different datasets.
Features
- MIT-BIH Dataset Training: The product focuses on training using the MIT-BIH dataset.
- Data Setup and Train: Instructions for setting up the data and training the ECG classification model.
- Predictions: Allows for predicting annotations of CINC2017 data or user-provided data for ECG classification.
- Jupyter Notebook Example: Provides an example of using the model in a Jupyter notebook for those without a high-performance GPU.
- Flask Web App: Includes a web application based on a Github repository for ECG classification predictions.
- Docker Support: Offers support for building and running the ECG trained model in a Docker container.
- Flask
Flask is a lightweight and popular web framework for Python, known for its simplicity and flexibility. It is widely used to build web applications, providing a minimalistic approach to web development with features like routing, templates, and support for extensions.
