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

TL;DR

This paper introduces an open source blood pressure waveform simulator and validation framework that uses 3D printed parts and Python software to facilitate sensor development and validation with high accuracy.

Contribution

It presents a novel, cost-effective open source simulator and validation package for continuous blood pressure sensors using 3D printed components and real waveform generation.

Findings

High similarity between measured and nominal waveforms.

RMSE values indicate low error in waveform reproduction.

Pearson correlation coefficients show very high waveform similarity.

Abstract

Measuring the blood pressure waveform is becoming a more frequently studied area. The development of sensor technologies opens many new ways to be able to measure high-quality signals. The development of such an aim-specific sensor can be time-consuming, expensive, and difficult to test or validate with known and consistent waveforms. In this paper, we present an open source blood pressure waveform simulator with an open source Python validation package to reduce development costs for early-stage sensor development and research. The simulator mainly consists of 3D printed parts which technology has become a widely available and cheap solution. The core part of the simulator is a 3D printed cam that can be generated based on real blood pressure waveforms. The validation framework can create a detailed comparison between the signal waveform used to design the cam and the measured time series from the sensor being validated. The presented simulator proved to be robust and accurate in short- and long-term use, as it produced the signal waveform consistently and accurately. To validate this solution, a 3D force sensor was used, which was proven earlier to be able to measure high-quality blood pressure waveforms on the radial artery at the wrist. The results showed high similarity between the measured and the nominal waveforms, meaning that comparing the normalized signals, the RMSE value ranged from $0.0276 \pm 0.0047$ to $0.0212 \pm 0.0023$, and the Pearson correlation ranged from $0.9933 \pm 0.0027$ to $0.9978 \pm 0.0005$. Our validation framework is available at https://github.com/repat8/cam-bpw-sim. Our hardware framework, which allows reproduction of the presented solution, is available at https://github.com/repat8/cam-bpw-sim-hardware. The entire design is an open source project and was developed using free software.