heart-murmur-analysis 0.1.1

Heartbeat analysis using Deep Learning, Signal Processing, and AI Agents.

🎵 Heart Murmur Detection with LSTM

A deep learning application that uses LSTM neural networks to detect heart murmurs from audio recordings. The application provides a user-friendly Streamlit interface for uploading audio files and getting real-time predictions with signal processing.

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📁 Project Structure

heart_app/
│
├── main.py                         # Streamlit entry point
│
├── classification/
│   ├── __init__.py
│   ├── model_loader.py             # Loads the trained LSTM model
│   ├── classifier.py               # Runs prediction + preprocessing
│
├── signal_processing/
│   ├── __init__.py
│   ├── loader.py                   # Load & normalize wav
│   ├── preprocessing.py            # Bandpass filter, envelope extraction
│   ├── features.py                 # Peak detection, HRV, SNR, Energy, etc.
│   ├── visualizer.py               # Plot waveform, spectrogram, histograms
│   ├── analyzer.py                 # Combines all steps, generates report
│
├── utils/
│   ├── __init__.py
│   ├── printer.py                  # Pretty print reports
│
├── models/
│   └── lstm_model.h5               # Pre-trained LSTM model
│
├── reports/
│   ├── heartbeat_report.json       # Generated reports (saved outputs)
│
├── requirements.txt
└── README.md

Quick Start

Prerequisites

• Python 3.8 or higher
• Windows 10/11 (PowerShell)
• Git (optional, for cloning)

Installation Steps

1. Clone or Download the Project

   git clone <repository-url>
   cd Heart-Murmur-Disease
   

   Or download and extract the project folder.

2. Create Virtual Environment

   python -m venv hvenv
   

3. Activate Virtual Environment

   hvenv\Scripts\Activate.ps1
   

   You should see (hvenv) at the beginning of your command prompt.

4. Install Dependencies

   pip install -r requirements.txt
   

5. Run the Application

   streamlit run app.py
   

6. Access the Application

   • Open your web browser
   • Navigate to http://localhost:8501
   • Upload a WAV or MP3 audio file
   • Get instant heart murmur predictions with signal processing !

Install Dependencies

The requirements.txt file contains all necessary packages:

Install them with:

pip install -r requirements.txt

Step 4: Run the Application

streamlit run app.py

About Model Architecture

The LSTM model uses a hybrid CNN-LSTM architecture:

• Input: Raw audio data (52 timesteps, 1 feature)
• CNN Layers: 3 Conv1D layers with MaxPooling and BatchNormalization
• LSTM Layers: 2 LSTM layers for sequence modeling
• Dense Layers: 3 fully connected layers with dropout
• Output: 3 classes (Normal, Abnormal, Murmur)
• Total Parameters: 14,130,371 (53.90 MB)

Performance Tips

• Sample Rate: The model expects 22050 Hz (automatically handled)

Technical Details

Input Preprocessing

• Audio is loaded at 22050 Hz sample rate
• Truncated or padded to exactly 52 samples
• Reshaped to (1, 52, 1) for model input

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

Happy Heart Murmur Detection!