Cardiac Arrhythmia Detection Dashboard (Flask)

A Flask-based web dashboard for detecting and visualizing cardiac arrhythmias from ECG data. This project provides a UI to upload ECG recordings, run a pre-trained model to classify arrhythmia types, and display results and visualizations.

Features

• Flask web interface for uploading ECG files (CSV / WFDB / numpy)
• Preprocessing pipeline for ECG signals
• Model inference for arrhythmia classification
• Visualization of ECG traces and prediction results
• Simple dashboard with history/log of processed samples

Requirements

• Python 3.8+
• Flask
• numpy, pandas
• scikit-learn or TensorFlow/PyTorch (depending on model)
• plotting libs: matplotlib / plotly

Install dependencies:

pip install -r requirements.txt

Running locally

1. Set up a virtual environment:

python -m venv .venv
.\.venv\Scripts\activate
pip install -r requirements.txt

2. Run the app:

set FLASK_APP=app.py
set FLASK_ENV=development
flask run

Open http://127.0.0.1:5000 in your browser.

Project structure (high level)

• app.py / run.py — Flask entry point
• templates/ — HTML templates for dashboard
• static/ — CSS, JS, images
• models/ — saved model files
• utils/ — preprocessing and helper functions

Notes

• Replace model files in models/ with your trained weights.
• Ensure uploaded ECG files match expected sampling rate and channels.

License

Specify your license here (e.g., MIT).