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Determining the Concentration of Red Blood Cells using Dielectric Properties

  • Jeantide Said Camilleri
  • , Lourdes Farrugia
  • , Julian Bonello
  • , Nikolai P. Pace
  • , Adam Santorelli
  • , Emily Porter
  • , Martin O'Halloran
  • , Charles V. Sammut
  • University of Malta
  • University of Galway

Research output: Chapter in Book or Conference Publication/ProceedingConference Publicationpeer-review

6 Citations (Scopus)

Abstract

This paper investigates an innovative method to determine the red and white blood cell concentrations in blood using their dielectric properties at microwave frequencies. The dielectric properties characterise the interaction of a time-varying electric field with the biological tissue. The concentrations of red blood cells (RBCs) and white blood cells in a sample of whole blood can vary due to illness or disease. This study is a proof-of-concept, where the dielectric properties of samples containing RBCs and plasma are investigated. The dielectric properties of samples of different concentration of RBCs in plasma are measured and used to train artificial neural networks which relate the measured properties to the known concentrations of RBCs. The results show that a trained neural network can predict the RBC concentrations in arbitrary samples not used to train the model with an average error of 1.37 % with respect to the actual concentration in the samples.

Original languageEnglish
Title of host publication14th European Conference on Antennas and Propagation, EuCAP 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9788831299008
DOIs
Publication statusPublished - Mar 2020
Externally publishedYes
Event14th European Conference on Antennas and Propagation, EuCAP 2020 - Copenhagen, Denmark
Duration: 15 Mar 202020 Mar 2020

Publication series

Name14th European Conference on Antennas and Propagation, EuCAP 2020

Conference

Conference14th European Conference on Antennas and Propagation, EuCAP 2020
Country/TerritoryDenmark
CityCopenhagen
Period15/03/2020/03/20

Keywords

  • Dielectric measurements
  • artificial neural networks
  • biological material
  • human blood
  • red blood cells

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