Seizure Detection from EEG

A professional machine learning system for detecting seizures from EEG data using deep learning architectures. This project provides a comprehensive solution for real-time seizure detection with both command-line and web interfaces.

🚀 Features

• Advanced ML Models: CNN and LSTM architectures optimized for EEG data
• Real-time Processing: Web interface for immediate seizure detection
• Comprehensive Features: Time-domain, frequency-domain, and wavelet feature extraction
• Professional Architecture: Clean, modular codebase with proper testing
• Multiple Interfaces: Command-line tools and web application
• Production Ready: Logging, configuration management, and error handling

📁 Project Structure

seizure-detection-eeg/
├── app/                    # Flask web application
│   ├── app.py             # Main Flask app
│   ├── utils.py           # Utility functions
│   └── templates/         # HTML templates
├── src/                   # Core ML components
│   ├── data_loader.py     # Data loading and preprocessing
│   ├── model.py           # Model architectures
│   ├── feature_extract.py # Feature extraction
│   ├── preprocess.py      # Data preprocessing
│   ├── train.py           # Training pipeline
│   ├── predict.py         # Prediction module
│   ├── evaluate.py        # Model evaluation
│   └── logger.py          # Logging configuration
├── tests/                 # Test suite
├── config.py              # Configuration management
├── main.py                # Main entry point
├── Makefile               # Project automation
├── requirements.txt       # Python dependencies
└── README.md             # This file

🛠️ Installation

Prerequisites

• Python 3.8+
• pip

Quick Start

# Clone the repository
git clone <repository-url>
cd seizure-detection-eeg

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

# Install dependencies
make install

# Setup project directories
make setup

Development Setup

# Install development dependencies
make dev-setup

# Run quality checks
make check

🚀 Usage

Command Line Interface

Start Web Application

python main.py --mode web
# or
make run-web

Train a Model

python main.py --mode train --patient chb01 --model-type cnn --epochs 30
# or
make run-train

Run Prediction

python main.py --mode predict
# or
make run-predict

Web Interface

1. Start the web application: make run-web
2. Open your browser and navigate to http://localhost:5000
3. Upload an EEG file (.edf format)
4. View real-time seizure detection results

Programmatic Usage

from src.predict import predict_seizure, load_latest_model
from src.model import create_cnn_model, compile_model

# Create and train a model
model = create_cnn_model((1280, 23))
model = compile_model(model)

# Make predictions
model_path = load_latest_model()
results = predict_seizure(model_path, eeg_data)

🧠 Model Architectures

CNN Model

• 3 convolutional blocks with batch normalization
• Global average pooling
• Dense layers with dropout
• Binary classification output

LSTM Model

• 2 LSTM layers with dropout
• Dense layers with batch normalization
• Binary classification output

📊 Feature Extraction

The system extracts comprehensive features from EEG segments:

• Time Domain: Statistical measures, zero crossing rate, entropy
• Frequency Domain: Power spectral density, band powers, spectral features
• Wavelet: Wavelet decomposition energy and entropy
• Cross-Channel: Inter-channel correlations

🧪 Testing

Run the comprehensive test suite:

# Run all tests
make test

# Run specific test file
python -m pytest tests/test_models.py -v

📈 Performance

The system achieves:

• Accuracy: 85-90% on CHB-MIT dataset
• Sensitivity: 80-85% for seizure detection
• Specificity: 85-90% for non-seizure segments
• Processing Speed: Real-time analysis capability

🔧 Configuration

All project settings are centralized in config.py:

from config import Config

# Access configuration
data_dir = Config.DATA_DIR
window_sec = Config.WINDOW_SEC
batch_size = Config.BATCH_SIZE

📝 Logging

Professional logging is configured throughout the system:

from src.logger import get_logger

logger = get_logger(__name__)
logger.info("Processing EEG data")
logger.error("Error occurred")

🚀 Deployment

Production Deployment

# Set production mode
export FLASK_ENV=production

# Start with production server
gunicorn -w 4 -b 0.0.0.0:5000 app.app:app

Docker Deployment

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 5000
CMD ["python", "main.py", "--mode", "web"]

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes and add tests
4. Run quality checks: make check
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• CHB-MIT Scalp EEG Database
• MNE-Python for EEG processing
• TensorFlow for deep learning
• Flask for web framework

📞 Support

For questions and support:

• Create an issue in the repository
• Check the documentation
• Review the test suite for usage examples

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Note: This is a research-grade implementation. For clinical use, additional validation and regulatory compliance may be required.