ElectroVector

AI-Powered ECG Analysis & Vectorcardiography Conversion

ElectroVector is an innovative iOS application that leverages machine learning to digitize electrocardiogram (ECG) scans and convert them into vectorcardiogram (VCG) diagrams. The app combines real-time ECG monitoring simulation with advanced image processing capabilities to provide comprehensive cardiac analysis.

• Real-time ECG Monitoring: Simulated ECG waveform display with heart rate monitoring
• Camera Integration: Capture ECG images directly from physical ECG printouts
• Image Upload: Process captured ECG images for digitization
• User-friendly Interface: Intuitive SwiftUI-based design with beautiful animations
• Permission Management: Seamless camera permission handling

• ECG Digitization: Advanced machine learning models in the MLbackend folder convert ECG images to digital data
• VCG Conversion: Transform digitized ECG signals into vectorcardiogram representations
• Server Processing: Flask-based backend handles image processing and ML inference
• Real-time Results: Fast processing and response delivery

ElectroVector/
├── iOS App (SwiftUI)
│   ├── ECGScannerView.swift      # Main interface
│   ├── ECGService.swift          # Network communication
│   ├── CameraView.swift          # Camera capture
│   ├── ECGWaveformView.swift     # Waveform visualization
│   └── Supporting Files
└── MLbackend/                    # Server-side ML models
    ├── Flask Server
    ├── ECG Digitization Models
    └── VCG Conversion Algorithms

• iOS 18.4+
• Xcode 16.3+
• Swift 5.0+
• Camera access permission

• Python 3.8+
• Flask framework
• Machine learning dependencies (see MLbackend folder)
• ngrok for tunneling (development)

1. Clone the repository
2. Open ElectroVector.xcodeproj in Xcode
3. Update the development team in project settings
4. Build and run on device or simulator

1. Navigate to the MLbackend folder
2. Install required Python dependencies
3. Start the Flask server
4. Update the server URL in ECGService.swift to point to your backend

• For simulator testing: Use 127.0.0.1 or localhost
• For device testing: Use your computer's IP address or ngrok tunnel
• Update serverURL in ECGService.swift accordingly

1. Launch the App: Open ElectroVector on your iOS device
2. Start Monitoring: Tap "Start Monitoring" to begin real-time ECG simulation
3. Capture ECG: Use "Capture ECG Image" to photograph a physical ECG printout
4. Processing: The app uploads the image to the ML backend for analysis
5. Results: Receive digitized ECG data and VCG diagram conversion

1. Image Capture: High-quality JPEG capture of ECG printouts
2. Preprocessing: Image enhancement and noise reduction
3. Signal Extraction: ML models identify and extract ECG waveforms
4. Digitization: Convert visual signals to numerical time-series data
5. VCG Conversion: Transform ECG leads into 3D vectorcardiogram

• Endpoint: /digitize (POST)
• Format: Multipart form data with image field
• Response: JSON with processing status and results
• Error Handling: Comprehensive error responses with detailed messages

• Local Processing: ECG simulation runs entirely on device
• Secure Upload: Images uploaded via HTTPS for ML processing
• No Data Storage: Images processed and discarded, no permanent storage
• Permission-Based: Camera access only when explicitly granted

We welcome contributions! Please see the MLbackend folder for server-side improvements and submit pull requests for iOS app enhancements.

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For technical support or questions about the ECG digitization algorithms, please refer to the documentation in the MLbackend folder or create an issue in this repository.

ElectroVector - Transforming cardiac care through AI-powered ECG analysis