OpenCardiographySignalMeasuringDevice

OpenCardiographySignalMeasuringDevice

OpenCardiographySignalMeasuringDevice

An open-source device for measuring cardiography signals with a GUI for easier handling and additional software for analyzing the data.

OpenCardiographySignalMeasuringDevice

Project Overview

This project represents a comprehensive solution for measuring and analyzing cardiography signals, combining hardware design, software development, and data analysis. The device can measure multiple physiological signals simultaneously, providing a complete picture of cardiovascular health.

Key Features

  • Multi-Signal Measurement: Simultaneously captures ECG, PPG, and stethoscope signals
  • Real-time Processing: Live signal processing and visualization
  • Data Analysis: Comprehensive analysis software for signal interpretation
  • Open Source: Complete hardware and software designs available
  • GUI Interface: User-friendly interface for easier operation
  • 3D Printed Enclosure: Custom-designed housing for professional appearance

Technical Specifications

Hardware Components

  • Microcontroller: Raspberry Pi Pico for data acquisition
  • Sensors:
    • ECG electrodes for heart electrical activity
    • PPG sensor for blood oxygen saturation and heart rate
    • Stethoscope for acoustic heart monitoring
    • Air pressure sensor for blood pressure measurement
  • Custom PCB: Designed for optimal signal quality and noise reduction
  • 3D Printed Case: Professional enclosure with proper sensor placement

Software Features

  • Real-time Signal Processing: Live filtering and analysis
  • Data Visualization: Interactive graphs and charts
  • Signal Analysis: Peak detection, heart rate calculation, blood pressure estimation
  • Data Export: Save measurements for further analysis
  • GUI Application: Intuitive interface for non-technical users

Signal Analysis Capabilities

ECG Signal Processing

  • R-peak detection for heart rate calculation
  • Heart rate variability analysis
  • Morphology analysis of individual heartbeats
  • Validation of measurements from other sensors

PPG Signal Analysis

  • Blood oxygen saturation measurement
  • Heart rate monitoring
  • Blood pressure estimation during cuff deflation
  • Detection of laminar vs turbulent blood flow

Stethoscope Signal Processing

  • Acoustic heart monitoring
  • Systolic pressure detection
  • Heart sound analysis
  • Integration with pressure measurements

Air Pressure Analysis

  • Blood pressure cuff pressure monitoring
  • Systolic and diastolic pressure estimation
  • Mean arterial pressure calculation
  • Integration with other physiological signals

Results & Validation

The device has been validated against commercial blood pressure monitors with impressive accuracy:

ParameterCommercial DeviceOur DeviceAccuracy
Systolic Pressure130 mmHg132 mmHg98.5%
Diastolic Pressure72 mmHg79 mmHg90.3%
Heart Rate81 BPM80 BPM98.8%
Mean Arterial Pressure93 mmHg91 mmHg97.8%

Project Impact

  • 290+ GitHub Stars: Significant community interest
  • 32 Forks: Active development by the community
  • Open Source: Making healthcare technology accessible
  • Educational Value: Comprehensive documentation and tutorials
  • Research Applications: Suitable for academic and clinical research

Technologies Used

  • Hardware: Raspberry Pi Pico, Custom PCB Design, 3D Printing
  • Software: Python, C++, Jupyter Notebooks
  • Signal Processing: Digital filtering, peak detection, statistical analysis
  • GUI Development: Python-based interface
  • Data Analysis: Comprehensive signal processing algorithms

Repository Structure

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OpenCardiographySignalMeasuringDevice/
├── CAD/                    # 3D models and CAD files
├── Data & Data Analysis/   # Sample data and analysis scripts
├── Electronics/           # PCB designs and schematics
├── Pictures/              # Project documentation images
└── Software/              # Application code and GUI

Getting Started

  1. Hardware Assembly: Follow the CAD files and PCB designs
  2. Software Installation: Install the Python dependencies
  3. Calibration: Calibrate sensors according to documentation
  4. First Measurement: Use the GUI to perform initial measurements

Future Development

  • Enhanced signal processing algorithms
  • Mobile app integration
  • Cloud data storage and analysis
  • Machine learning for improved accuracy
  • Additional sensor integration

Links & Resources

Contact

For questions about this project or collaboration opportunities:

This project demonstrates the intersection of hardware design, signal processing, and medical device development, showcasing the potential of open-source healthcare technology.