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Open AccessArticle

Application of Artificial Neural Networks for Accurate Determination of the Complex Permittivity of Biological Tissue

1
Department of Physics, Faculty of Science, University of Malta, MSD 2080 Msida, Malta
2
Institute of Space Sciences and Astronomy, University of Malta, MSD 2080 Msida, Malta
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4640; https://doi.org/10.3390/s20164640
Received: 20 July 2020 / Revised: 11 August 2020 / Accepted: 13 August 2020 / Published: 18 August 2020
Medical devices making use of radio frequency (RF) and microwave (MW) fields have been studied as alternatives to existing diagnostic and therapeutic modalities since they offer several advantages. However, the lack of accurate knowledge of the complex permittivity of different biological tissues continues to hinder progress in of these technologies. The most convenient and popular measurement method used to determine the complex permittivity of biological tissues is the open-ended coaxial line, in combination with a vector network analyser (VNA) to measure the reflection coefficient (S11) which is then converted to the corresponding tissue permittivity using either full-wave analysis or through the use of equivalent circuit models. This paper proposes an innovative method of using artificial neural networks (ANN) to convert measured S11 to tissue permittivity, circumventing the requirement of extending the VNA measurement plane to the coaxial line open end. The conventional three-step calibration technique used with coaxial open-ended probes lacks repeatability, unless applied with extreme care by experienced persons, and is not adaptable to alternative sensor antenna configurations necessitated by many potential diagnostic and monitoring applications. The method being proposed does not require calibration at the tip of the probe, thus simplifying the measurement procedure while allowing arbitrary sensor design, and was experimentally validated using S11 measurements and the corresponding complex permittivity of 60 standard liquid and 42 porcine tissue samples. Following ANN training, validation and testing, we obtained a prediction accuracy of 5% for the complex permittivity. View Full-Text
Keywords: complex permittivity measurement; artificial neural network; in-vivo; biological samples complex permittivity measurement; artificial neural network; in-vivo; biological samples
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MDPI and ACS Style

Bonello, J.; Demarco, A.; Farhat, I.; Farrugia, L.; Sammut, C.V. Application of Artificial Neural Networks for Accurate Determination of the Complex Permittivity of Biological Tissue. Sensors 2020, 20, 4640.

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