A Highly Sensitive Pressure-Sensing Array for Blood Pressure Estimation Assisted by Machine-Learning Techniques

Sensors
Kuan-Hua HuangYao-Joe Yang

Abstract

This work describes the development of a pressure-sensing array for noninvasive continuous blood pulse-wave monitoring. The sensing elements comprise a conductive polymer film and interdigital electrodes patterned on a flexible Parylene C substrate. The polymer film was patterned with microdome structures to enhance the acuteness of pressure sensing. The proposed device uses three pressure-sensing elements in a linear array, which greatly facilitates the blood pulse-wave measurement. The device exhibits high sensitivity (-0.533 kPa-1) and a fast dynamic response. Furthermore, various machine-learning algorithms, including random forest regression (RFR), gradient-boosting regression (GBR), and adaptive boosting regression (ABR), were employed for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) from the measured pulse-wave signals. Among these algorithms, the RFR-based method gave the best performance, with the coefficients of determination for the reference and estimated blood pressures being R² = 0.871 for SBP and R² = 0.794 for DBP, respectively.

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Citations

Jul 19, 2020·Clinical Journal of the American Society of Nephrology : CJASN·Jay A PanditDaniel Batlle
Apr 16, 2021·ACS Nano·Yu WangYuanjin Zhao
May 8, 2021·Advanced Healthcare Materials·Shuwen ChenChwee Teck Lim
Aug 15, 2021·Analytical and Bioanalytical Chemistry·Yuemeng ChengDaxiang Cui
Jan 14, 2020·ACS Applied Materials & Interfaces·Xijian LiuJing Yu
Nov 2, 2021·ACS Nano·Yuhong WeiTian-Ling Ren

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Methods Mentioned

BETA
scanning electron microscopy

Software Mentioned

learn
Scikit

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