STANDALONE_ECG_FM_PACKAGE/README.md

🏥 STANDALONE ECG-FM PACKAGE FOR MIDITA SERVER INTEGRATION

🎯 Purpose

This standalone package allows you to test ECG-FM independently before integrating it into your midita_server. Once you're satisfied with the results, you can easily integrate it with minimal changes.

🏗️ Package Structure

STANDALONE_ECG_FM_PACKAGE/
├── README.md                           # This file
├── requirements.txt                    # Dependencies
├── ecg_fm_client.py                   # Standalone ECG-FM client
├── test_standalone.py                  # Independent testing script
├── sample_ecg_data/                    # Sample ECG files for testing
├── integration_guide.md                # How to integrate with midita_server
└── examples/                           # Usage examples

🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Test ECG-FM Independently

python test_standalone.py

3. Use ECG-FM Client in Your Code

from ecg_fm_client import ECGFMClient

# Initialize client
client = ECGFMClient()

# Analyze ECG
results = client.analyze_ecg(ecg_data)
print(f"Clinical Results: {results}")

🔧 What This Package Provides

✅ ECG-FM Core Functionality:

• 17 Clinical Labels with confidence scores
• 256-Dimensional Feature Embeddings
• Saliency Maps (AI attention visualization)
• Clinical Measurements (HR, QRS, QT, risk scores)
• Signal Quality Assessment

✅ Easy Integration:

• Clean API Interface - Simple function calls
• Error Handling - Robust fallback mechanisms
• Data Validation - Input format checking
• Performance Monitoring - Processing time tracking

✅ Testing Capabilities:

• Sample ECG Data - Ready-to-use test files
• Comprehensive Testing - All ECG-FM features
• Performance Benchmarks - Speed and accuracy metrics
• Error Simulation - Test edge cases

📊 Expected Output Format

{
  "status": "success",
  "ecg_id": "ecg_123",
  "processing_time_ms": 1250,
  "clinical_analysis": {
    "probabilities": [0.95, 0.12, 0.03, ...],
    "labels": ["Sinus rhythm", "Tachycardia", ...],
    "confidence": 0.89,
    "primary_findings": "Sinus tachycardia with good signal quality"
  },
  "feature_analysis": {
    "embeddings": [0.123, -0.456, ...],
    "dimension": 256,
    "feature_statistics": {...}
  },
  "saliency_maps": {
    "attention_weights": [...],
    "attention_max": [...],
    "temporal_focus": "R-wave and ST-segment regions"
  },
  "clinical_measurements": {
    "heart_rate": 120,
    "qrs_duration": 85,
    "signal_quality": "Excellent",
    "clinical_risk": 6.5
  }
}

🔗 Integration with Midita Server

Phase 1: Testing (Current)

• Test ECG-FM independently
• Validate clinical accuracy
• Performance benchmarking
• Error handling validation

Phase 2: Integration

• Add ECG-FM endpoints to midita_server
• Integrate with existing ECG workflow
• Add to user interface
• Performance optimization

Phase 3: Production

• Clinical validation
• User training
• Performance monitoring
• Continuous improvement

📚 Documentation Files

• README.md - This overview file
• integration_guide.md - Detailed integration instructions
• examples/ - Code examples and use cases
• sample_ecg_data/ - Test ECG files

🆘 Support & Troubleshooting

Common Issues:

1. Model Loading Errors - Check HF token and internet connection
2. Memory Issues - Ensure sufficient RAM (4GB+ recommended)
3. Performance Issues - Check CPU/GPU availability

Getting Help:

• Check error logs in console output
• Verify ECG data format (12 leads, 5000 samples)
• Ensure all dependencies are installed correctly

---

🎉 Ready to Test!

This package gives you everything you need to:

1. Test ECG-FM independently ✅
2. Validate clinical accuracy ✅
3. Benchmark performance ✅
4. Prepare for integration ✅

Start with python test_standalone.py and let me know how it goes! 🚀