CardioGuard- A cardiac arrest prediction and alert system

CardioGuard is an AI-powered cardiac arrest prediction and alert system designed to provide real-time monitoring and timely notifications to help prevent cardiac emergencies.

Table of Contents

• Features
• System Architecture
• Installation
  • Prerequisites
  • Frontend Setup
  • Hardware Setup
  • Backend & ML Setup
• Usage
• Contributing
• License
• Acknowledgements

Features

• Real-Time Monitoring: Continuously tracks ECG and SpO₂ levels using AD8232 sensors integrated with an ESP32 microcontroller.
• Machine Learning Integration: Utilizes a Random Forest model with K-Fold validation for accurate anomaly detection.
• Automated Alerts: Leverages Supabase Edge Functions and Firebase Cloud Messaging to send instant notifications to healthcare providers and emergency contacts.
• Progressive Web App (PWA): Provides a user-friendly interface accessible on various devices.
• Scalability: Designed with future enhancements in mind, including deep learning integration, multi-sensor fusion, and edge AI processing.

System Architecture

CardioGuard is built on a multi-layered architecture:

1. Wearable Sensor Module:

   • Captures ECG and SpO₂ data via AD8232 sensors and ESP32.
   • Transmits data in real time to the cloud via Wi-Fi/Bluetooth.

2. Cloud Server:

   • Processes incoming sensor data using a Node.js/Python backend.
   • Performs machine learning inference using a pre-trained Random Forest classifier.

3. Notification Service:

   • Uses Supabase Edge Functions and Firebase Cloud Messaging (FCM) to trigger alerts when anomalies are detected.

4. User Interface:

   • React-based Progressive Web App (PWA) for real-time data visualization and alert management.

Installation

Prerequisites

• Node.js v16+ and npm
• Python 3.8+ (for ML model training)
• Arduino IDE (for ESP32 firmware)
• Firebase and Supabase accounts

Frontend Setup

1. Clone the Repository:

   git clone https://github.com/your-username/CardioGuard.git
   cd CardioGuard/frontend

2. Install Dependencies:

   npm install

3. Configure Environment Variables: Create a .env file with your Firebase and Supabase credentials:

   VITE_FIREBASE_API_KEY=your_key
   VITE_SUPABASE_URL=your_url
   VITE_SUPABASE_KEY=your_key
   

4. Start the Development Server:

   npm run dev

   Access the app at http://localhost:3000.

Hardware Setup (ESP32)

1. Upload Firmware:
   • Open CardioGuard/firmware/sensor_module.ino in Arduino IDE.
   • Install required libraries (AD8232, ESP32 BLE).
   • Set Wi-Fi credentials in the code.
   • Upload to ESP32.

Backend & ML Setup

1. Navigate to the Backend Directory:

   cd ../backend

2. Install Python Dependencies:

   pip install -r requirements.txt

3. Train the ML Model:

   python train_model.py

4. Start the Server:

   node server.js

Usage

1. Pair the Sensor Module:

   • Power on the ESP32 device.
   • Open the CardioGuard PWA and navigate to Devices > Pair New Device.

2. Monitor Real-Time Data:

   • View live ECG and SpO₂ graphs on the dashboard.

3. Configure Alerts:

   • Go to Settings > Notifications to add emergency contacts and providers.

4. Historical Analysis:

   • Access Reports to review past data and anomaly logs.

Contributing

1. Fork the Repository.
2. Create a Feature Branch:

   git checkout -b feature/amazing-feature

3. Commit Your Changes:

   git commit -m "Add amazing feature"

4. Push to the Branch:

   git push origin feature/amazing-feature

5. Open a Pull Request.

License

Distributed under the MIT License. See LICENSE for details.

Acknowledgements

• The AD8232 library maintainers.
• Firebase and Supabase for backend services.
• Scikit-learn for ML tooling.