Intelligent Remote Monitoring and Management system for Type1 Diabetes

The work presented in this thesis focuses on developing a telemedicine system for better management of type1 diabetes in children and teenagers. The research and development of the system is motivated by the inadequate communication in the current system of management of the disease, which results in non-compliance of patients following the regimen. This non-compliance generally results in uncontrolled blood glucose levels, which can result in hypoglycaemia, hyperglycaemia and later life health complications. This further results in an increase in health care costs. In this context, the thesis presents a novel end-to-end, low cost telemedicine system, WithCare+, developed in close collaboration between the University of Sheffield (Electronics & Electrical Engineering) and Sheffield Children’s Hospital. The system was developed to address the challenges of implementing modern telemedicine in type 1 diabetic care with particular relevance to National Health Service children’s clinics in the United Kingdom, by adopting a holistic care driven approach (involving all stakeholders) based on specific key enabler technologies such as low cost and reconfigurable design. However, one of the major issues with current telemedicine system is non-compliance of the patients due to invasive procedure of the glucose measurement which could be clearly addressed by non-invasive method of glucose measurement. Hence, the thesis also makes a contribution towards non-invasive glucose measurement using Near Infrared spectroscopy in terms of addressing the calibration challenge; two methods are proposed to improve the calibration of the Near Infrared instrument. The first method combines locally weighted regression and partial least square regression and the second method combines digital band pass filtering with support vector regression. The efficacy of the proposed methods is validated in experiments carried out in a non-controlled environment and the results obtained demonstrate that the proposed methods improved the performance of the calibration model in comparison to traditional calibration techniques such as Principal Component Regression and Partial Least Squares regression

Field laboratories for non-invasive wildlife and habitat health assessment and conservation

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Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

Electronic nose implementation for biomedical applications

The growing rate of diabetes and undiagnosed diabetes related diseases is becoming a worldwide major health concern. The motivation of this thesis was to make use of a technology called the ‘electronic nose’ (eNose) for diagnosing diseases. It presents a comprehensive study on metabolic and gastro-intestinal disorders, choosing diabetes as a target disease. Using eNose technology with urinary volatile organic compounds (VOCs) is attractive as it allows non-invasive monitoring of various molecular constituents in urine. Trace gases in urine are linked to metabolic reactions and diseases. Therefore, urinary volatile compounds were used for diagnosis purposes in this thesis. The literature on existing eNose technologies, their pros and cons and applications in biomedical field was thoroughly reviewed, especially in detecting headspace of urine. Since the thesis investigates urinary VOCs, it is important to discover the stability of urine samples and their VOCs in time. It was discovered that urine samples lose their stability and VOCs emission after 9 months. A comprehensive study with 137 diabetic and healthy control urine samples was done to access the capability of commercially available eNose instruments for discrimination between these two groups. Metal oxide gas sensor based commercial eNose (Fox 4000, AlphaMOS Ltd) and field asymmetric ion mobility spectrometer (Lonestar, Owlstone Ltd) were used to analyse volatiles in urinary headspace. Both technologies were able to distinguish both groups with sensitivity and specificity of more than 90%. Then the project moved onto developing a Non-dispersive infrared (NDIR) sensor system that is non-invasive, low-cost, precise, rapid, simple and patient friendly, and can be used at both hospitals and homes. NDIR gas sensing is one of the most widely used optical gas detection techniques. NDIR system was used for diagnosing diabetes and gastro related diseases from patient’s wastes. To the best of the authors’ knowledge, this is the first and only developed tuneable NDIR eNose system. The developed optical eNose is able to scan the whole infrared range between 3.1μm and 10.5 μm with step size of 20 nm. To simulate the effect of background humidity and temperature on the sensor response, a gas test rig system that includes gas mixture, VOC generator, humidity generator and gas analyser was designed to enable the user to have control of gas flow, humidity and temperature. This also helps to find out system’s sensitivity and selectivity. Finally, after evaluating the sensitivity and selectivity of optical eNose, it was tested on simple and complex odours. The results were promising in discriminating the odours. Due to insufficient sample batches received from the hospital, synthetic urine samples were purchased, and diabetic samples were artificially made. The optical eNose was able to successfully separate artificial diabetic samples from non-diabetic ones

Development of non-invasive, optical methods for central cardiovascular and blood chemistry monitoring.

Cardiovascular disease and sepsis are leading causes of mortality, morbidity and high cost in hospitals around the world. Failure of the circulatory system during cardiogenic shock and sepsis both can significantly impair the perfusion of oxygen through organs, resulting in poor patient outcome if not detected and corrected early. Another common disorder which goes hand-in-hand with cardiovascular disease is Diabetes Mellitus. Diabetes is a metabolic disorder resulting from the inability of the body to regulate the level of glucose in the blood. The prevalence of diabetes worldwide is increasing faster than society’s ability to manage cost effectively, with an estimated 9% of the world population diagnosed with metabolic disease. The current gold standard measurements for venous oxygen saturation, arterial pulse wave velocity (PWV), and diabetes management through blood glucose concentration monitoring are all invasive. Invasive measurements increase risk of infection and com- plications, are often high cost and disposable, and have a low patient compliance to regular measurements. The aim of this thesis is to develop non-invasive methods of monitoring these important dynamic physiological variables, including, venous oxygen saturation, pulse wave velocity, and blood glucose concentration. A novel photoplethysmography-based NIR discrete wavelength spectrometer was developed using LEDs to both emit light, and detect the light reflected back through the tissue. Using LEDs to detect light simplifies sensing circuit design, lowering hardware costs, allowing adaptable sensing specific to the needs of the user. A reflectance pulse oximeter was developed to measure the oxygen saturation at both the external jugular vein, and carotid artery. Measuring the jugular venous pulse (JVP) allows estimation of the venous oxygen saturation through either the JVP, or through breathing induced variation of the JVP. In addition to oxygenation, the de- vice developed is capable of adapting the sensing layout to measure the arterial pulse waveform at multiple sites along a peripheral artery, such as the carotid or radial. The PWV local to the carotid artery, and radial artery can then be measured, providing key information of cardiovascular risk. A novel algorithm for PWV measurement over multiple pulse waveforms was also developed. Expanding the sensor to use multiple different wavelength LEDs allow discrete spectroscopy in pulsatile blood. An absorption model of components in blood at specific wavelengths was created to isolate the spectral fingerprint of glucose. The sensor successfully measured the oxygen saturation at the carotid artery, and external jugular vein across 15 subjects, giving mean oxygen saturations of 92% and 85% respectively, within the expected physiological ranges. Venous oxygen saturation calculated using breathing induced changes to JVP was 3.3% less than when calculated on the JVP alone, with a standard deviation of 5.3%, compared to 6.9%. Thus, future work on the sensor will focus on extraction of the breathing induced venous pulse, rather than measuring from the JVP itself. The PWV on the carotid and radial artery was successfully measured within the ex- pected physiological ranges, with the novel phase difference algorithm proving more robust to noise than the gold standard foot-foot method. The phase difference method returned a mean PWV at the radial artery of 4.7 ±0.6 m s−1, and a mean CoV of 20%, compared to 4.0 ±1.4 m s−1, and a moan CoV of 58% for the foot-foot method. The proof of concept PWV sensor gives promising results, but needs to be calibrated against invasive gold standards, such as aorta and femoral pressure catheters. A glucose trial involving adult and neonatal subjects provided validation of the NIR non-invasive pulse glucometer. The sensor has an R2 of 0.47, and a mean absolute relative difference (MARD) of 19% compared to gold standard reference measurements. Clarke error grid analysis returns 85% of measurements in Zone A, 11% in Zone B, and 4% in Zone C. While the sensor is not as accurate as the gold standard invasive measurements, the ability to constantly measure without any pain or discomfort will help increase measurement compliance, improving user quality of life, plus further development may improve this. Overall, this thesis provided novel contributions in non-invasive venous oxygen saturation, PWV, and glucose concentration monitoring. The adaptability of the sensor shows promise in helping reduce the pain and inconvenience of the current invasive measurements, especially in diabetes management, where the sensor has the most potential for impact

Implementations of Wireless and Wired Intelligent Systems for Healthcare with Focus on Diabetes and Ultrasound Applications

The research and implementations presented in this thesis focuses mainly on healthcare applications utilizing the wireless and wired communication and “Micro-Electro-Mechanical Systems” (MEMS) technologies, and secondly on security aspects. Chapters four and five presents new work in intelligent diabetes remote monitoring front-end system and into the corresponding new ultrasound simulator training systems. The motivation from the University of Sheffield of Electronic and Electrical Engineering Department and Sheffield Children Hospital with the partial grant scholarship from “Engineering and Physical Sciences Research Council” (EPSRC) for involvement in one “Collaborations for Leadership in Applied Health Research and Care” (CLAHRC) projects, was to improve the existing WithCare+ system and also the development of multiple new front-end solutions for it. My motivation to create solutions which will improve the life of patients who suffer from chronic disease such as type-1 diabetes, and also to provide new methods in management of that illness by clinicians and possible resulting annual government money saving, drives me to the successful result. From the other side, the motivation from the department of Neonatal in Sheffield Royal Hallamshire Hospital and the University of Sheffield of Electronic and Electrical Engineering Department drives me to the creation of a new, very low cost ultrasound simulation training system, using new components such as MEMS sensors. The hardware design and embedded source code was created in order to provide a ready library, for use by other projects, where 3D space orientation is required through exploitation of MEMS sensors and intelligent fusion filter algorithm. The third contribution affects the cryptographic aspects. The new implementation of fast and very efficient portable C code algorithm for t-adic NAF Key generation in ECC cryptographic principle for utilization of it with Koblitz curves presented in Appendix I

Persuasive digital health technologies for lifestyle behaviour change

BACKGROUND. Unhealthy lifestyle behaviours such as physical inactivity are global risk factors for chronic disease. Despite this, a substantial proportion of the UK population fail to achieve the recommended levels of physical activity. This may partly be because the health messages presently disseminated are not sufficiently potent to evoke behaviour change. There has been an exponential growth in the availability of digital health technologies within the consumer marketplace. This influx of technology has allowed people to self-monitor a plethora of health indices, such as their physical activity, in real-time. However, changing movement behaviours is difficult and often predicated on the assumption that individuals are willing to change their lifestyles today to reduce the risk of developing disease years or even decades later. One approach that may help overcome this challenge is to present physiological feedback in parallel with physical activity feedback. In combination, this approach may help people to observe the acute health benefits of being more physically active and subsequently translate that insight into a more physically active lifestyle. AIMS. Study One aimed to review existing studies employing fMRI to examine neurological responses to health messages pertaining to physical activity, sedentary behaviour, smoking, diet and alcohol consumption to assess the capacity for fMRI to assist in evaluating health behaviours. Study Two aimed to use fMRI to evaluate physical activity, sedentary behaviour and glucose feedback obtained through wearable digital health technologies and to explore associations between activated brain regions and subsequent changes in behaviour. Study Three aimed to explore engagement of people at risk of type 2 diabetes using digital health technologies to monitor physical activity and glucose levels. METHODS. Study One was a systematic review of published studies investigating health messages relating to physical activity, sedentary behaviour, diet, smoking or alcohol consumption using fMRI. Study Two asked adults aged 30-60 years to undergo fMRI whilst presented personalised feedback on their physical activity, sedentary behaviour and glucose levels, following a 14-day wear protocol of an accelerometer, inclinometer and flash glucose monitor. Study Three was a six-week, three-armed randomised feasibility trial for individuals at moderate-to-high risk of developing type 2 diabetes. The study used commercially available wearable physical activity (Fitbit Charge 2) and flash glucose (Freestyle Libre) technologies. Group 1 were offered glucose feedback for 4 weeks followed by glucose plus physical activity feedback for 2 weeks (G4GPA2). Group 2 were offered physical activity feedback for 4 weeks followed by glucose plus physical activity feedback for 2 weeks (PA4GPA2). Group 3 were offered glucose plus physical activity feedback for six weeks (GPA6). The primary outcome for the study was engagement, measured objectively by time spent on the Fitbit app, LibreLink app (companion app for the Freestyle Libre) as well as the frequency of scanning the Freestyle Libre and syncing the Fitbit. RESULTS. For Study One, 18 studies were included in the systematic review and of those, 15 examined neurological responses to smoking related health messages. The remaining three studies examined health messages about diet (k=2) and physical activity (k=1). Areas of the prefrontal cortex and amygdala were most commonly activated with increased activation of the ventromedial prefrontal cortex predicting subsequent behaviour (e.g. smoking cessation). Study Two identified that presenting people with personalised feedback relating to interstitial glucose levels resulted in significantly more brain activation when compared with feedback on personalised movement behaviours (P<.001). Activations within regions of the prefrontal cortex were significantly greater for glucose feedback compared with feedback on personalised movement behaviours. Activation in the subgyral area was correlated with moderate-to-vigorous physical activity at follow-up (r=.392, P=.043). In Study Three, time spent on the LibreLink app significantly reduced for G4GPA2 and GPA6 (week 1: 20.2±20 versus week 6: 9.4±14.6min/day, p=.007) and significantly fewer glucose scans were recorded (week 1: 9.2±5.1 versus week 6: 5.9±3.4 scans/day, p=.016). Similarly, Fitbit app usage significantly reduced (week 1: 7.1±3.8 versus week 6: 3.8±2.9min/day p=.003). The number of Fitbit syncs did not change significantly (week 1: 6.9±7.8 versus week 6: 6.5±10.2 syncs/day, p=.752). CONCLUSIONS. Study One highlighted the fact that thus far the field has focused on examining neurological responses to health messages using fMRI for smoking with important knowledge gaps in the neurological evaluation of health messages for other lifestyle behaviours. The prefrontal cortex and amygdala were most commonly activated in response to health messages. Using fMRI, Study Two was able to contribute to the knowledge gaps identified in Study One, with personalised glucose feedback resulting in a greater neurological response than personalised feedback on physical activity and sedentary behaviour. From this, Study Three found that individuals at risk of developing type 2 diabetes were able to engage with digital health technologies offering real-time feedback on behaviour and physiology, with engagement diminishing over time. Overall, this thesis demonstrates the potential for digital health technologies to play a key role in feedback paradigms relating to chronic disease prevention

Design of a Customized multipurpose nano-enabled implantable system for in-vivo theranostics

The first part of this paper reviews the current development and key issues on implantable multi-sensor devices for in vivo theranostics. Afterwards, the authors propose an innovative biomedical multisensory system for in vivo biomarker monitoring that could be suitable for customized theranostics applications. At this point, findings suggest that cross-cutting Key Enabling Technologies (KETs) could improve the overall performance of the system given that the convergence of technologies in nanotechnology, biotechnology, micro&nanoelectronics and advanced materials permit the development of new medical devices of small dimensions, using biocompatible materials, and embedding reliable and targeted biosensors, high speed data communication, and even energy autonomy. Therefore, this article deals with new research and market challenges of implantable sensor devices, from the point of view of the pervasive system, and time-to-market. The remote clinical monitoring approach introduced in this paper could be based on an array of biosensors to extract information from the patient. A key contribution of the authors is that the general architecture introduced in this paper would require minor modifications for the final customized bio-implantable medical device

Real-time signal detection and classification algorithms for body-centered systems

El principal motivo por el cual los sistemas de comunicación en el entrono corporal se desean con el objetivo de poder obtener y procesar señales biométricas para monitorizar e incluso tratar una condición médica sea ésta causada por una enfermedad o el rendimiento de un atleta. Dado que la base de estos sistemas está en la sensorización y el procesado, los algoritmos de procesado de señal son una parte fundamental de los mismos. Esta tesis se centra en los algoritmos de tratamiento de señales en tiempo real que se utilizan tanto para monitorizar los parámetros como para obtener la información que resulta relevante de las señales obtenidas. En la primera parte se introduce los tipos de señales y sensores en los sistemas en el entrono corporal. A continuación se desarrollan dos aplicaciones concretas de los sistemas en el entorno corporal así como los algoritmos que en las mismas se utilizan. La primera aplicación es el control de glucosa en sangre en pacientes con diabetes. En esta parte se desarrolla un método de detección mediante clasificación de patronones de medidas erróneas obtenidas con el monitor contínuo comercial "Minimed CGMS". La segunda aplicacióin consiste en la monitorizacióni de señales neuronales. Descubrimientos recientes en este campo han demostrado enormes posibilidades terapéuticas (por ejemplo, pacientes con parálisis total que son capaces de comunicarse con el entrono gracias a la monitorizacióin e interpretación de señales provenientes de sus neuronas) y también de entretenimiento. En este trabajo, se han desarrollado algoritmos de detección, clasificación y compresión de impulsos neuronales y dichos algoritmos han sido evaluados junto con técnicas de transmisión inalámbricas que posibiliten una monitorización sin cables. Por último, se dedica un capítulo a la transmisión inalámbrica de señales en los sistemas en el entorno corporal. En esta parte se estudia las condiciones del canal que presenta el entorno corporal para la transmisión de sTraver Sebastiá, L. (2012). Real-time signal detection and classification algorithms for body-centered systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16188Palanci