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• W2912984668 startingPage "848" @default.
• W2912984668 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." @default.
• W2912984668 created "2019-02-21" @default.
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• W2912984668 date "2019-02-19" @default.
• W2912984668 modified "2023-10-17" @default.
• W2912984668 title "A Highly Sensitive Pressure-Sensing Array for Blood Pressure Estimation Assisted by Machine-Learning Techniques" @default.
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• W2912984668 doi "https://doi.org/10.3390/s19040848" @default.
• W2912984668 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6412448" @default.
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• W2912984668 hasPublicationYear "2019" @default.
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