ECG-Generation

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leveraging diffusion models to generate diverse and realistic ECG signals based on user-defined prompts and specifications.

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# ECG Signal Generation using Diffusion Models

This repository contains code for generating Electrocardiogram (ECG) signals using diffusion models. The project focuses on leveraging diffusion models to generate diverse and realistic ECG signals based on user-defined prompts and specifications. The generation process incorporates stable diffusion techniques and utilizes pretrained encoders and decoders for signal transformation and reconstruction.

## Overview

The goal of this project is to develop a robust framework for synthesizing ECG signals with varying characteristics, including different heartbeat types and levels of noise. The generated signals aim to closely resemble real-world ECG recordings and can be customized based on user input.

![ECG Signal](https://raw.githubusercontent.com/PooryaAghaomidi/ECG-Generation/main/Images/main.jpg)

## Table of Contents

1. [Repository Structure](#repository-structure)
2. [Installation](#installation)
3. [Usage](#usage)
4. [License](#license)

## Repository Structure

- `Dataset/`: Contains main and preprocessed datasets. (Use this link to download the preprocessed dataset: https://www.kaggle.com/datasets/shayanfazeli/heartbeat/data)
- `Images/`: Includes images of the paper and repository.
- `Source/`: Codes and model checkpoints.
- `requirements.txt`: List of dependencies.

## Installation

1. Create an environment with `python v3.9` interpreter.

2. Install pytorch:

```shell
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu117
```

3. Install requirements:

```shell
pip install -r requirements.txt
```

## Usage

```text
This project has not been completed yet.
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

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