PulseGuard: ECG Analysis System Deployment Guide

This guide will help you deploy the PulseGuard ECG analysis system, which analyzes ECG images to detect myocardial infarction (MI).

System Requirements

• Python 3.8 or higher
• 4GB RAM minimum (8GB recommended)
• GPU support optional but recommended for faster inference

Directory Structure

Set up your project with the following structure:

pulse-guard/
├── additional_func.py     # Core functions (from your original code)
├── streamlit_app.py       # Streamlit application
├── requirements.txt       # Package dependencies
├── models/
│   ├── yolo_ecg_model.pt  # Your trained YOLO model
│   └── cnn_lstm_model.pt  # Your trained CNN-LSTM model
└── README.md              # Project documentation

Installation Steps

1. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

   pip install -r requirements.txt

3. Place your model files:

   Place your trained YOLO and CNN-LSTM model files in the models/ directory:

   • YOLO model at models/yolo_ecg_model.pt
   • CNN-LSTM model at models/cnn_lstm_model.pt

4. Update model paths (if needed):

   If your model files are in different locations, update the YOLO_MODEL_PATH and ML_MODEL_PATH variables in streamlit_app.py.

Running the Application Locally

Start the Streamlit application:

streamlit run streamlit_app.py

The application will be available at http://localhost:8501 by default.

Deployment Options

Option 1: Streamlit Community Cloud

1. Create a GitHub repository with your project files
2. Sign up for Streamlit Community Cloud
3. Deploy directly from your GitHub repository

Option 2: Docker Deployment

1. Create a Dockerfile:

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

EXPOSE 8501

CMD ["streamlit", "run", "streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

2. Build and run the Docker container:

docker build -t pulse-guard .
docker run -p 8501:8501 pulse-guard

Option 3: Platform as a Service (PaaS)

You can deploy to platforms like Heroku, Google Cloud Run, or AWS Elastic Beanstalk following their respective deployment guides.

Production Considerations

1. Model Size: The YOLOv8 and CNN-LSTM models can be large. Consider model optimization techniques like quantization for production.

2. Memory Management: Implement proper cleanup of temporary files and resources.

3. Authentication: Add user authentication for production deployment if dealing with sensitive data.

4. Monitoring: Implement logging and monitoring to track usage and detect errors.

Customization

To customize the application:

1. Logo: Add your own logo by uncommenting the logo section in streamlit_app.py and providing a logo file.

2. UI Colors: Customize the Streamlit theme by creating a .streamlit/config.toml file.

3. Additional Features: Extend the application with features like report generation, patient database integration, or multi-model ensemble predictions.

Support

For any issues or questions regarding deployment, please refer to the following resources:

• Streamlit Documentation
• PyTorch Deployment Guide
• YOLOv8 Documentation