Towards Accurate Dielectric Property Retrieval of Biological Tissues for Blood Glucose Monitoring

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Wearable RF sensors for non-invasive detection of blood-glucose levels

PhDRadio frequency (RF) techniques have the potential to provide blood glucose readings through sensing the glucose dependent change in dielectric properties of the biological tissue. Such technique can enable much desired non-invasive and continuous monitoring of blood glucose level. In this work, we present realistic glucose dependence of dielectric properties as well as basic understanding of resonator behaviour while radiating towards the lossy biological tissue. To investigate the potential of RF techniques, two resonators, operating at microwave frequencies when placed radiating towards the biological tissue, are designed and fabricated. The spiral resonator is tested with liquid and semi-solid phantoms containing different amounts of sugar. An analytical formulation to retrieve the dielectric properties of the biological tissues is improved. In order to perform realistic tests, novel tissue mimicking materials for an extremely wide frequency range are proposed. Glucose dependance of the blood mimicking material dielectric properties are further investigated by adding realistic glucose amounts to the blood mimicking material and dielectric spectroscopy is performed. Next, a single pole Cole-Cole model is fitted to the median of the dielectric property measurements. In addition, a patch resonator is simulated with four-layered digital phantom and tested with the four-layered physical tissue mimicking phantom. Finally, a double parameter measurement platform is constructed by combining the patch resonator and a commercial force sensor to perform controlled experiments with humans. Also, the force dependant response of the patch resonator is quantified. Soda tests is performed on five subjects with the platform, all subjects were asked to apply the same level of force. Spiral resonator is also applied to examine the glucose changes of two human subjects during the soda test. The results suggests that, although the glucose-dependance of the dielectric properties is relatively small, the input impedance of a microwave resonator is still sensitive to such small alterations

Microwave dielectric spectroscopy of renal calculi: A large scale study on dielectric properties from 500 MHz to 18 GHz

Inherent dielectric property discrepancy between biological anomalies and healthy tissue enables the microwave diagnostic and therapeutic technologies. To reveal this discrepancy, microwave dielectric properties of many different biological tissues are tabulated. Although the dielectric properties of biological tissues are well documented in the literature, the knowledge on microwave dielectric property behavior of the renal calculi is limited. This work presents ultra wideband dielectric properties of three renal calculi types between 500 MHz to 18 GHz to pave the way for possible application of microwave technologies for diagnosis, treatment, and prevention of urolithiasis. Microwave dielectric spectroscopy is performed on a total of 66 natural stone samples with open-ended coaxial probe technique. The samples belong to three commonly diagnosed renal calculi categories, namely calcium oxalate, cystine, struvite. Analysis of variance (ANOVA) test is performed on fitted Cole-Cole parameters and it was concluded that there is a statistically significant difference between the dielectric properties of the renal calculi types. A patient-to-patient statistical test is also performed and it was concluded that there is no statistical difference between the samples belonging to the same renal calculi category. To this end, based on the relative permittivity discrepancy between the renal calculi types, the category of renal calculi can be identified by measuring the dielectric properties of renal calculi with open-ended coaxial probe technique

Microwave spectroscopy based classification of rat hepatic tissues: On the significance of dataset

With the advancements in machine learning (ML) algorithms, microwave dielectric spectroscopy emerged as a potential new technology for biological tissue and material categorization. Recent studies reported the successful utilization of dielectric properties and Cole-Cole parameters. However, the role of the dataset was not investigated. Particularly, both dielectric properties and Cole-Cole parameters are derived from the S parameter response. This work investigates the possibility of using S parameters as a dataset to categorize the rat hepatic tissues into cirrhosis, malignant, and healthy categories. Using S parameters can potentially remove the need to derive the dielectric properties and enable the utilization of microwave structures such as narrow or wideband antennas or resonators. To this end, in vivo dielectric properties and S parameters collected from hepatic tissues were classified using logistic regression (LR) and adaptive boosting (AdaBoost) algorithms. Cole-Cole parameters and a reproduced dielectric property data set were also investigated. Data preprocessing is performed by using standardization a principal component analysis (PCA). Using the AdaBoost algorithm over 93% and 88% accuracy is obtained for dielectric properties and S parameters, respectively. These results indicate that the classification can be performed with a 5% accuracy decrease indicating that S parameters can be an alternative dataset for tissue classification.Kırklareli Üniversites

Enhancing the accuracy of non-invasive glucose sensing in aqueous solutions using combined millimeter wave and near infrared transmission

We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.This research was funded partially by Innovate UK project 104554 (2019–2021)

Characterisation of the In-vivo Terahertz Communication Channel within the Human Body Tissues for Future Nano-Communication Networks.

PhDBody centric communication has been extensively studied in the past for a range of frequencies, however the need to reduce the size of the devices makes nano-scale technologies attractive for future applications. This opens up opportunities of applying nano-devices made of the novel materials, like carbon nano tubes (CNT), graphene and etc., which operate at THz frequencies and probably inside human bodies. With a brief introduction of nano-communications and review of the state of the art, three main contributions have been demonstrated in this thesis to characterise nano-scale body-centric communication at THz band: • A novel channel model has been studied. The path loss values obtained from the simulation have been compared with an analytical model in order to verify the feasibility of the numerical analysis. On the basis of the path loss model and noise model, the channel capacity is also investigated. • A 3-D stratified skin model is built to investigate the wave propagation from the under-skin to skin surface and the influence of the rough interface between different skin layers is investigated by introducing two detailed skin models with different interfaces (i.e.,3-D sine function and 3-D sinc function). In addition, the effects of the inclusion of the sweat duct is also analysed and the results show great potential of the THz waves on sensing and communicating. • Since the data of dielectric properties for biological materials at THz band are quite scarce, in collaboration with the Blizard Institute, London, UK, different human tissues such as skin, blood, muscle and etc. are planned to be measured with the THz Time Domain Spectroscopy (THz-TDS) system at Queen Mary University of London to enrich the database of electromagnetic parameters at the band of interest. In this chapter, collagen, the main constitution of skin was i mainly studied. Meanwhile, the measured results are compared with the simulated ones with a good agreement. Finally, a plan for further research activities is presented, aiming at widening and deepening the present understanding of the THz body-centric nano-communication channel, thus providing a complete characterisation useful for the design of reliable and efficient body centric nano-networks. iiChina Scholarship Council Queen Mary University of Londo

Electromagnetic Wearable Sensors: A Solution to Non-Invasive Real-Time Monitoring of Biological Markers during Exercise

Wearable sensing technology enables greater insights into the performance and health status of athletes during training and competition, which are currently unattainable through traditional laboratory-based techniques. The process of collecting accurate data from complex metabolic parameters usually requires the use of specialised equipment and methods that are often expensive and invasive. This research proposes the novel use of a purpose-built electromagnetic (EM) sensor to non-invasively detect biological markers in humans during exercise. Three parameters were selected for investigation: sweat sodium, blood lactate, and skeletal muscle glycogen. Each of these parameters were selected based on their significance to athletic performance monitoring, as well as their current methods of analysis being impractical for real-time monitoring during exercise. Four human studies and two in-vitro sample-based studies were conducted, accumulating in 140 sweat samples, 523 blood lactate samples, and 21 glycogen samples, collected from a combined total of 71 participants, 56 males, and 15 females. The research presented within this thesis demonstrated that a hairpin EM sensor operating at microwave frequencies could detect and measure changes in sodium concentration within human sweat samples at 1.6 GHz (R2 = 0.862). Further sensor development is required for on-subject monitoring of sweat sodium during exercise (R2 = 0.149), findings suggest this was a result of the microwave sensor’s design, rather than sensing capabilities. Additionally, the sensor was shown to measure blood lactate concentration in untrained participants at 3.4-3.6 GHz (R2 = 0.78), and within endurance-trained participants at 3.2-3.8 GHz (R2 = 0.757). Furthermore, results showed that the sensor could detect changes in glycogen sample concentration at 2.11 GHz (R2 = 0.87) and monitor skeletal muscle glycogen in humans when concentrations were grouped into exercise specific ranges at 2.0-2.25 GHz (R2 = 0.91). This research presents an accurate, cost-effective, and efficient method of detecting biological markers non-invasively and continuously during exercise. With future research and development, a single microwave sensor could ultimately lead to improvements in human performance monitoring, enabling individualised and real-time fuelling strategies during training and competition. Further assessment of this technology is needed within a real-world setting to understand if this remains a feasible solution outside of a controlled environment