This repository contains the source code and associated files for the research study titled "Learning with Incomplete Labels of Multisource Datasets for ECG Classification".

• Introduction
• Getting Started
• Prerequisites
• Installation
• Data Preparation
• Usage

This research study focuses on addressing the challenge of classifying electrocardiogram (ECG) data from multiple sources with incomplete labels. The study proposes a deep-learning-based framework to improve the accuracy of ECG classification in such scenarios. This repository contains the source code and resources related to the research.

These instructions will help you get a copy of the project up and running on your local machine for development and testing purposes.

Before you begin, ensure you have met the following requirements:

• Python (>=3.6)
• TensorFlow (>=2.0)
• Other dependencies (listed in requirements.txt)

1. Clone the repository:

   git clone https://github.com/sdnjly/multisource-ecg-classification.git
   cd multisource-ecg-classification

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate

3. Install required packages:

   pip install -r requirements.txt

The datasets used in this study can be found from the Physionet/CinC challenge 2020/2021 websites. You can follow instructions on the websites to downloads the datasets.

• Physionet/CinC challenge 2020
• Physionet/CinC challenge 2021

1. model training

   python train_model.py training_data model

   training_data is a folder of training data files, model is a folder for saving your models, test_data is a folder of test data files

2. model testing

   python test_model.py model test_data test_outputs