https://github.com/babban33/physionet-2024

https://github.com/babban33/physionet-2024

Last synced: 19 days ago
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• Host: GitHub
• URL: https://github.com/babban33/physionet-2024
• Owner: Babban33
• Created: 2024-04-08T05:53:58.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-04-20T06:31:17.000Z (10 months ago)
• Last Synced: 2024-04-20T07:25:46.351Z (10 months ago)
• Language: Python
• Size: 2.93 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Physionet Challenge 2024 Submission

## Introduction

This repository contains our submission for the Physionet Challenge 2024, aimed at designing and implementing open-source algorithms for reconstructing ECG waveforms and classifying cardiovascular diseases (CVDs) from physical ECGs. 

## Dataset

Our algorithms were developed and evaluated using the PTBXL dataset and the WFDB dataset, widely recognized datasets in the field of cardiovascular research, providing comprehensive collections of ECG recordings for robust algorithm development and evaluation.

## Repository Structure

- **`challenge-1` Folder:** Contains the implementation for Event 1, focusing on reconstructing ECG waveforms from images.

  - See the `challenge-1/readme.md` file for specific instructions and details regarding this event.

- **`challenge-2` Folder:** Contains the implementation for Event 2, focusing on classifying ECGs as normal or abnormal.

  - Refer to the `challenge-2/readme.md` file for detailed instructions and insights pertaining to this event.

## Running Scripts

**Step 1: Install Virtual Environment package**:

``` pip install virtualenv```

**Step 2: Create Virtual ENvironmnet**:

``` virtualenv dep```

**Note**: replace *dep* with your virtual env name.

**Step 3: Activate Virtual Environment**:

``` 

    cd dep

    Scripts/activate

    cd ..

```

**Step 4: Run Python files in given sequence**:

``` python csvtowave.py ```

``` python test.py ```

## Usage

Each folder (`challenge-1` and `challenge-2`) contains its own set of instructions, scripts, and documentation to guide users on how to replicate our results, run the algorithms, and interact with the provided codebase. Please refer to the respective `readme.md` files within each folder for specific usage instructions.

## Conclusion

We are excited to submit our open-source algorithms for the Physionet Challenge 2024. Our aim is to contribute to the advancement of cardiovascular research by providing innovative solutions for reconstructing ECG waveforms and classifying CVDs from physical ECGs. We hope that our submission will foster collaboration and inspire further developments in this critical area of healthcare.