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Microwave Sensors for Biomedical Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 30601

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Dr. Sandra Costanzo

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Department of Computer Engineering, Modeling, Electronics and Systems (DIMES), University of Calabria, 87036 Arcavacata, Italy
Interests: microwave and millimeter-waves antennas and circuits; microwave biomedical applications; innovative materials for antennas; electromagnetics in health safety
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Special Issue Information

Dear Colleagues,

In recent years, the adoption of microwave sensors in medicine has been assessed as a convenient approach to non-invasive sensing, diagnostics, and therapy, mainly due to the relatively innocuous nature of microwave radiation and its penetration ability through biological media. Nevertheless, technological advancements are still required to fully exploit the promising advantages of microwaves by properly facing their complex interactions with the human body, which are accurately modeled in terms of its electromagnetic properties when considering the design of microwave sensors.

This Special Issue calls for reporting on recent advances and future challenges in the design of microwave sensors for biomedical applications.

The list of possible topics includes but is not limited to the following:

The dielectric characterization of tissues at microwave and millimeter waves;
Accurate dielectric models of biological media;
Non-invasive microwave sensors for medical diagnostics;
Microwave sensors for therapeutic applications.

Prof. Dr. Sandra Costanzo
Guest Editor

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Keywords

microwaves
electromagnetic diagnostics
non-invasive sensors
dielectric characterization
electromagnetic interactions with biological media

Published Papers (7 papers)

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Research

13 pages, 588 KiB

Open AccessArticle

Multiclass Classification of Hepatic Anomalies with Dielectric Properties: From Phantom Materials to Rat Hepatic Tissues

by Tuba Yilmaz

Sensors 2020, 20(2), 530; https://doi.org/10.3390/s20020530 - 18 Jan 2020

Cited by 12 | Viewed by 2917

Abstract

Open-ended coaxial probes can be used as tissue characterization devices. However, the technique suffers from a high error rate. To improve this technology, there is a need to decrease the measurement error which is reported to be more than 30% for an in vivo measurement setting. This work investigates the machine learning (ML) algorithms’ ability to decrease the measurement error of open-ended coaxial probe techniques to enable tissue characterization devices. To explore the potential of this technique as a tissue characterization device, performances of multiclass ML algorithms on collected in vivo rat hepatic tissue and phantom dielectric property data were evaluated. Phantoms were used for investigating the potential of proliferating the data set due to difficulty of in vivo data collection from tissues. The dielectric property measurements were collected from 16 rats with hepatic anomalies, 8 rats with healthy hepatic tissues, and in house phantoms. Three ML algorithms, k-nearest neighbors (kNN), logistic regression (LR), and random forests (RF) were used to classify the collected data. The best performance for the classification of hepatic tissues was obtained with 76% accuracy using the LR algorithm. The LR algorithm performed classification with over 98% accuracy within the phantom data and the model generalized to in vivo dielectric property data with 48% accuracy. These findings indicate first, linear models, such as logistic regression, perform better on dielectric property data sets. Second, ML models fitted to the data collected from phantom materials can partly generalize to in vivo dielectric property data due to the discrepancy between dielectric property variability. Full article

(This article belongs to the Special Issue Microwave Sensors for Biomedical Applications)

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12 pages, 4311 KiB

Open AccessArticle

Noncontact Detection of Respiration Rate Based on Forward Scatter Radar

by Fan Yang, Zhiming He, Yuanhua Fu, Liang Li, Kui Jiang and Fangyan Xie

Sensors 2019, 19(21), 4778; https://doi.org/10.3390/s19214778 - 03 Nov 2019

Cited by 16 | Viewed by 2965

Abstract

Bioradar-based noncontact breathing detection technology has been widely studied due to its superior detection performance. In this paper, a breath detection mechanism based on the change in radar cross section (RCS) is proposed by using a forward scatter radar and the deduction of the mathematical model of the received signal. Furthermore, we completed human breathing detection experiments in an anechoic chamber and in an ordinary chamber; we obtained the breathing rate through envelope detection in cases where the human orientation angle was 0, 30, 60, and 90°. The analysis of the measured data shows that the theoretical model fits well with the measured results. Compared with the existing single-base radar detection schemes, the proposed scheme can detect human respiratory rates in different orientations. Full article

(This article belongs to the Special Issue Microwave Sensors for Biomedical Applications)

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14 pages, 6010 KiB

Open AccessArticle

A Single-Connector Stent Antenna for Intravascular Monitoring Applications

by Chien-Hao Liu, Shuo-Chih Chen and Hao-Ming Hsiao

Sensors 2019, 19(21), 4616; https://doi.org/10.3390/s19214616 - 23 Oct 2019

Cited by 13 | Viewed by 4842

Abstract

Recently, smart stents have been developed by integrating various sensors with intravascular stents for detecting vascular restenosis or monitoring intravascular biomedical conditions such as blood pressure or blood flow velocity. The information on biomedical signals is then transmitted to external monitoring systems via wireless communications. Due to the limited volumes of blood vessels and limited influence of blood flow, antennas with good radiation performance are required for intravascular applications. In this paper, we propose a stent antenna composed of multiple rings containing crowns and struts, where each ring is connected with one connector. Unlike a conventional stent, wherein each ring is connected with several connectors, the single connector prevents the random distribution of electrical current and thus achieves good radiation performance. The implantable stent antenna is designed for the frequency range of 2 to 3 GHz for minimum penetration loss in the human body and tissues. Mechanical FEM simulations were conducted to ensure that the mechanical deformation was within specific limits during balloon expansions. A prototype was fabricated with laser cutting techniques and its radiation performance experimentally characterized. It was demonstrated that the fabricated stent antenna had an omnidirectional radiation pattern for arbitrary receiving angles, a gain of 1.38 dBi, and a radiation efficiency of 74.5% at a resonant frequency of 2.07 GHz. The main contribution of this work was the manipulation of the current distributions of the stent for good EM radiation performances which needed to be further examined while inserted inside human bodies. These research results should contribute to the further development of implantable wireless communications and intravascular monitoring of biomedical signals such as blood pressure and blood flow velocity. Full article

(This article belongs to the Special Issue Microwave Sensors for Biomedical Applications)

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14 pages, 2409 KiB

Open AccessArticle

3D Simulations of Intracerebral Hemorrhage Detection Using Broadband Microwave Technology

by Andreas Fhager, Stefan Candefjord, Mikael Elam and Mikael Persson

Sensors 2019, 19(16), 3482; https://doi.org/10.3390/s19163482 - 09 Aug 2019

Cited by 14 | Viewed by 3573

Abstract

Early, preferably prehospital, detection of intracranial bleeding after trauma or stroke would dramatically improve the acute care of these large patient groups. In this paper, we use simulated microwave transmission data to investigate the performance of a machine learning classification algorithm based on subspace distances for the detection of intracranial bleeding. A computational model, consisting of realistic human head models of patients with bleeding, as well as healthy subjects, was inserted in an antenna array model. The Finite-Difference Time-Domain (FDTD) method was then used to generate simulated transmission coefficients between all possible combinations of antenna pairs. These transmission data were used both to train and evaluate the performance of the classification algorithm and to investigate its ability to distinguish patients with versus without intracranial bleeding. We studied how classification results were affected by the number of healthy subjects and patients used to train the algorithm, and in particular, we were interested in investigating how many samples were needed in the training dataset to obtain classification results better than chance. Our results indicated that at least 200 subjects, i.e., 100 each of the healthy subjects and bleeding patients, were needed to obtain classification results consistently better than chance (p < 0.05 using Student’s t-test). The results also showed that classification results improved with the number of subjects in the training data. With a sample size that approached 1000 subjects, classifications results characterized as area under the receiver operating curve (AUC) approached 1.0, indicating very high sensitivity and specificity. Full article

(This article belongs to the Special Issue Microwave Sensors for Biomedical Applications)

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12 pages, 1304 KiB

Open AccessArticle

Early, Non-Invasive Sensing of Sustained Hyperglycemia in Mice Using Millimeter-Wave Spectroscopy

by Aldo Moreno-Oyervides, Pedro Martín-Mateos, M. Carmen Aguilera-Morillo, Giacomo Ulisse, María C. Arriba, María Durban, Marcela Del Rio, Fernando Larcher, Viktor Krozer and Pablo Acedo

Sensors 2019, 19(15), 3347; https://doi.org/10.3390/s19153347 - 30 Jul 2019

Cited by 3 | Viewed by 3109

Abstract

Diabetes is a very complex condition affecting millions of people around the world. Its occurrence, always accompanied by sustained hyperglycemia, leads to many medical complications that can be greatly mitigated when the disease is treated in its earliest stage. In this paper, a novel sensing approach for the early non-invasive detection and monitoring of sustained hyperglycemia is presented. The sensing principle is based on millimeter-wave transmission spectroscopy through the skin and subsequent statistical analysis of the amplitude data. A classifier based on functional principal components for sustained hyperglycemia prediction was validated on a sample of twelve mice, correctly classifying the condition in diabetic mice. Using the same classifier, sixteen mice with drug-induced diabetes were studied for two weeks. The proposed sensing approach was capable of assessing the glycemic states at different stages of induced diabetes, providing a clear transition from normoglycemia to hyperglycemia typically associated with diabetes. This is believed to be the first presentation of such evolution studies using non-invasive sensing. The results obtained indicate that gradual glycemic changes associated with diabetes can be accurately detected by non-invasively sensing the metabolism using a millimeter-wave spectral sensor, with an observed temporal resolution of around four days. This unprecedented detection speed and its non-invasive character could open new opportunities for the continuous control and monitoring of diabetics and the evaluation of response to treatments (including new therapies), enabling a much more appropriate control of the condition. Full article

(This article belongs to the Special Issue Microwave Sensors for Biomedical Applications)

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9 pages, 2698 KiB

Open AccessArticle

Software-Defined Doppler Radar Sensor for Human Breathing Detection

by Sandra Costanzo

Sensors 2019, 19(14), 3085; https://doi.org/10.3390/s19143085 - 12 Jul 2019

Cited by 25 | Viewed by 5422

Abstract

Non-contact wireless sensing approaches have emerged in recent years, in order to enable novel enhanced developments in the framework of healthcare and biomedical scenarios. One of these technologically advanced solutions is given by software-defined radar platforms, a low-cost radar implementation, where all operations are implemented and easily changed via software. In the present paper, a software-defined radar implementation with Doppler elaboration features is presented, to be applied for the non-contact monitoring of human respiration signals. A quadrature receiver I/Q (In-phase/Quadrature) architecture is adopted in order to overcome the critical issues related to the occurrences of null detection points, while the phase-locked loop components included in the software defined radio transceiver are successfully exploited to guarantee the phase correlation between I/Q signal components. The proposed approach leads to a compact, low-cost, and flexible radar solution, whose application abilities may be simply changed via software, with no need for hardware modifications. Experimental results on a human target are discussed so as to demonstrate the feasibility of the proposed approach for vital signs detection. Full article

(This article belongs to the Special Issue Microwave Sensors for Biomedical Applications)

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21 pages, 6329 KiB

Open AccessArticle

Ultra-Wideband Temperature Dependent Dielectric Spectroscopy of Porcine Tissue and Blood in the Microwave Frequency Range

by Sebastian Ley, Susanne Schilling, Ondrej Fiser, Jan Vrba, Jürgen Sachs and Marko Helbig

Sensors 2019, 19(7), 1707; https://doi.org/10.3390/s19071707 - 10 Apr 2019

Cited by 43 | Viewed by 5727

Abstract

The knowledge of frequency and temperature dependent dielectric properties of tissue is essential to develop ultra-wideband diagnostic technologies, such as a non-invasive temperature monitoring system during hyperthermia treatment. To this end, we characterized the dielectric properties of animal liver, muscle, fat and blood in the microwave frequency range from 0.5 GHz to 7 GHz and in the temperature range between 30 °C and 50 °C. The measured data were modeled to a two-pole Cole-Cole model and a second-order polynomial was introduced to fit the Cole-Cole parameters as a function of temperature. The parametric model provides access to the dielectric properties of tissue at any frequency and temperature in the specified range. Full article

(This article belongs to the Special Issue Microwave Sensors for Biomedical Applications)

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