body-sound-detection 0.4.5

AI-powered body sound analysis platform for heart, lung, and bowel sound detection.

🩺 AI Body Sound Analyzer

An end-to-end medical diagnostics platform using Deep Learning, Digital Signal Processing (DSP), and Agentic AI to analyze biological sounds (Heart, Lung, and Bowel).

---

🚀 Key Features

1. ❤️ Heart Sound Analysis

• Deep Learning: Uses a trained LSTM (Long Short-Term Memory) model to classify heart sounds into Normal, Murmur, or Artifact.
• Signal Processing: Extracts S1/S2 heartbeats using energy envelope detection and 20-500Hz bandpass filtering.
• Heart Rate Variability (HRV): Detailed timing analysis of heart rate patterns.

2. 🫁 Lung Sound Analysis

• Classification: Powered by a GRU (Gated Recurrent Unit) model to detect Normal, Wheeze, or Crackle sounds.
• Breath Cycle Detection: Sophisticated prominence-based detection of inspiration and expiration phases.

3. 🩺 Bowel Sound Analysis

• Event Detection: High-resolution detection of bowel sounds using CRNN (Convolutional Recurrent Neural Networks).
• Metric Extraction: Analysis of event frequency and rhythmic patterns.

4. 💬 Specialized AI Agents

• Integrated with LangGraph and Groq (Llama-3) for rapid medical insight.
• Context-Aware: Agents receive patient information (age, gender, name) to provide personalized explanations.
• RAG Powered: Uses semantic search over analysis reports to answer technical questions about detected anomalies.

---

📦 Installation

Install the package directly from PyPI or locally in editable mode:

From PyPI

pip install body-sound-detection

Locally (for development)

pip install -e .

[!IMPORTANT] Ensure you have FFmpeg installed on your system for audio processing (required by librosa).

---

🏃 Usage

Programmatic Usage

You can use the package as a library in your own Python projects:

from body_sound_detection import HeartbeatAnalyzer, load_model

# Run Signal Processing
analyzer = HeartbeatAnalyzer("heartbeat_sample.wav")
results = analyzer.analyze()
print(f"Detected Heart Rate: {results['heart_rate_bpm']} BPM")

---

⚙️ Configuration

The AI Agents require the following environment variables (or a .env file):

• GROQ_API_KEY: For LLM generation.
• HF_TOKEN: For downloading models from HuggingFace.

---

📂 Project Structure

├── body_sound_detection/ 
│   ├── agent/             # LangGraph AI logic
│   ├── classification/    # Deep Learning (H5 Models)
│   ├── signal_processing/ # Audio DSP routines
│   ├── report_generator/  # PDF/JSON report logic
│   └── cli.py             # App entry point
├── main.py                # Main Streamlit dashboard
├── pyproject.toml         # Packaging metadata
└── README.md              # You are here!

---

📄 License

Distributed under the MIT License. See LICENSE for more information.