Ecg Mit Bih

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.

Summary

The ECG classification product focuses on training and using a model with the MIT-BIH dataset. It provides detailed instructions for setting up the environment, training the model, predicting ECG annotations, and using a web application for classification. Additionally, Docker support is available for easy deployment of the trained model in a containerized environment. Further references to original research papers and relevant GitHub repositories are included for additional information.