An Open Source Validation System for Continuous Arterial Blood Pressure Measuring Sensors

Area of Science:

• Biomedical Engineering
• Sensor Technology
• Medical Device Development

Background:

• High-quality continuous blood pressure monitoring is crucial in healthcare.
• Developing and validating application-specific blood pressure sensors is challenging, costly, and time-consuming.
• Existing methods lack consistent and reliable waveforms for sensor testing.

Purpose of the Study:

• To present an open-source blood pressure waveform simulator with a Python validation package.
• To provide a cost-effective and efficient solution for testing and validating blood pressure sensors.
• To enable reliable, high-fidelity generation of human arterial blood pressure waveforms.

Main Methods:

• Development of a core 3D-printed cam mechanism based on real blood pressure waveforms.
• Creation of a Python validation package for comparing simulated and sensor-derived waveforms.
• Validation of the simulator using a 3D force sensor to assess accuracy and precision.

Main Results:

• The simulator demonstrated high accuracy, with RMSE between 1.94-2.74% and Pearson correlation of 99.39-99.84% with nominal signals.
• Precision testing showed low RMSE (1.53-2.13%) and high Pearson correlation (99.59-99.85%) for cam rotation repeatability.
• The simulator proved robust and accurate for both short- and long-term use, reliably producing waveforms with high fidelity.

Conclusions:

• The open-source blood pressure waveform simulator significantly reduces development costs for early-stage sensor research.
• The easy-to-manufacture simulator offers a reliable method for generating continuous human arterial blood pressure waveforms.
• This tool facilitates the validation of new sensor technologies, accelerating innovation in healthcare monitoring.