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Jun 22, 2021;21 (13):

Advances in Non-Invasive Blood Pressure Monitoring.

Xina Quan, Junjun Liu, Thomas Roxlo, Siddharth Siddharth, Weyland Leong, Arthur Muir, So-Min Cheong, Anoop Rao
Author Information
  1. Xina Quan: PyrAmes Inc., Cupertino, CA 95014, USA.
  2. Junjun Liu: PyrAmes Inc., Cupertino, CA 95014, USA.
  3. Thomas Roxlo: PyrAmes Inc., Cupertino, CA 95014, USA.
  4. Siddharth Siddharth: PyrAmes Inc., Cupertino, CA 95014, USA.
  5. Weyland Leong: PyrAmes Inc., Cupertino, CA 95014, USA.
  6. Arthur Muir: PyrAmes Inc., Cupertino, CA 95014, USA.
  7. So-Min Cheong: Department of Geography & Atmospheric Science, University of Kansas, Lawrence, KS 66045, USA.
  8. Anoop Rao: Department of Pediatrics, Neonatology, Stanford University, Palo Alto, CA 94304, USA.
PMID: 34206457 DOI: 10.3390/s21134273

Abstract

This paper reviews recent advances in non-invasive blood pressure monitoring and highlights the added value of a novel algorithm-based blood pressure sensor which uses machine-learning techniques to extract blood pressure values from the shape of the pulse waveform. We report results from preliminary studies on a range of patient populations and discuss the accuracy and limitations of this capacitive-based technology and its potential application in hospitals and communities.

Keywords

NICU cNIBP hypertension hypotension neonate non-invasive blood pressure monitoring

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Grants

  1. R43 HD101175/NICHD NIH HHS
  2. 1R43HD101175-01/Eunice Kennedy Shriver National Institute of Child Health and Human Development

MeSH Term

Algorithms
Blood Pressure
Blood Pressure Determination
Heart Rate
Humans
Machine Learning
Journal Article Review
Article has an altmetric score of 8

Word Cloud

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