A Preventive Medicine Framework for Wearable Abiotic Glucose Detection System

In this work, we present a novel abiotic glucose fuel cell with battery-less remote access. In the presence of a glucose analyte, we characterized the power generation and biosensing capabilities. This system is developed on a flexible substrate in bacterial nanocellulose with gold nanoparticles used as a conductive ink for piezoelectric deposition based printing. The abiotic glucose fuel cell is constructed using colloidal platinum on gold (Au-co-Pt) and a composite of silver oxide nanoparticles and carbon nanotubes as the anodic and cathodic materials. At a concentration of 20 mM glucose, the glucose fuel cell produced a maximum open circuit voltage of 0.57 V and supplied a maximum short circuit current density of 0.581 mA/cm2 with a peak power density of 0.087 mW/cm2 . The system was characterized by testing its performance using electrochemical techniques like linear sweep voltammetry, cyclic voltammetry, chronoamperometry in the presence of various glucose level at the physiological temperatures. An open circuit voltage (Voc) of 0.43 V, short circuit current density (Isc) of 0.405 mA/cm2 , and maximum power density (Pmax) of 0.055 mW/cm2 at 0.23 V were achieved in the presence of 5 mM physiologic glucose. The results indicate that glucose fuel cells can be employed for the development of a self-powered glucose sensor. The glucose monitoring device demonstrated sensitivity of 1.87 uA/mMcm2 and a linear dynamic range of 1 mM to 45 mM with a correlation coefficient of 0.989 when utilized as a self-powered glucose sensor. For wireless communication, the incoming voltage from the abiotic fuel cell was fed to a low power microcontroller that enables battery less communication using NFC technology. The voltage translates to the NFC module as the digital signals, which are displayed on a custom-built android application. The digital signals are converted to respective glucose concentration using a correlation algorithm that allows data to be processed and recorded for further analysis. The android application is designed to record the time, date stamp, and other independent features (e.g. age, height, weight) with the glucose measurement to allow the end-user to keep track of their glucose levels regularly. Analytics based on in-vitro testing were conducted to build a machine learning model that enables future glucose prediction for 15, 30 or 60 minutes

Online Glucose Prediction in Type-1 Diabetes by Neural Network Models

Diabetes mellitus is a chronic disease characterized by dysfunctions of the normal regulation of glucose concentration in the blood. In Type 1 diabetes the pancreas is unable to produce insulin, while in Type 2 diabetes derangements in insulin secretion and action occur. As a consequence, glucose concentration often exceeds the normal range (70-180 mg/dL), with short- and long-term complications. Hypoglycemia (glycemia below 70 mg/dL) can progress from measurable cognition impairment to aberrant behaviour, seizure and coma. Hyperglycemia (glycemia above 180 mg/dL) predisposes to invalidating pathologies, such as neuropathy, nephropathy, retinopathy and diabetic foot ulcers. Conventional diabetes therapy aims at maintaining glycemia in the normal range by tuning diet, insulin infusion and physical activity on the basis of 4-5 daily self-monitoring of blood glucose (SMBG) measurements, obtained by the patient using portable minimally-invasive lancing sensor devices. New scenarios in diabetes treatment have been opened in the last 15 years, when minimally invasive continuous glucose monitoring (CGM) sensors, able to monitor glucose concentration in the subcutis continuously (i.e. with a reading every 1 to 5 min) over several days (7-10 consecutive days), entered clinical research. CGM allows tracking glucose dynamics much more effectively than SMBG and glycemic time-series can be used both retrospectively, e.g. to optimize metabolic control therapy, and in real-time applications, e.g. to generate alerts when glucose concentration exceeds the normal range thresholds or in the so-called “artificial pancreas”, as inputs of the closed loop control algorithm. For what concerns real time applications, the possibility of preventing critical events is, clearly, even more appealing than just detecting them as they occur. This would be doable if glucose concentration were known in advance, approximately 30-45 min ahead in time. The quasi continuous nature of the CGM signal renders feasible the use of prediction algorithms which could allow the patient to take therapeutic decisions on the basis of future instead of current glycemia, possibly mitigating/ avoiding imminent critical events. Since the introduction of CGM devices, various methods for short-time prediction of glucose concentration have been proposed in the literature. They are mainly based on black box time series models and the majority of them uses only the history of the CGM signal as input. However, glucose dynamics are influenced by many factors, e.g. quantity of ingested carbohydrates, administration of drugs including insulin, physical activity, stress, emotions and inter- and intra-individual variability is high. For these reasons, prediction of glucose time course is a challenging topic and results obtained so far may be improved. The aim of this thesis is to investigate the possibility of predicting future glucose concentration, in the short term, using new models based on neural networks (NN) exploiting, apart from CGM history, other available information. In particular, we first develop an original model which uses, as inputs, the CGM signal and information on timing and carbohydrate content of ingested meals. The prediction algorithm is based on a feedforward NN in parallel with a linear predictor. Results are promising: the predictor outperforms widely used state of art techniques and forecasts are accurate and allow obtaining a satisfactory time anticipation. Then we propose a second model, which exploits a different NN architecture, a jump NN, which combines benefits of both feedforward NN and linear algorithm obtaining performance similar to the previously developed predictor, although the simpler structure. To conclude the analysis, information on doses of injected bolus of insulin are added as input of the jump NN and the relative importance of every input signal in determining the NN output is investigated by developing an original sensitivity analysis. All the proposed predictors are assessed on real data of Type 1 diabetics, collected during the European FP7 project DIAdvisor. To evaluate the clinical usefulness of prediction in improving diabetes management we also propose a new strategy to quantify, using an in silico environment, the reduction of hypoglycemia when alerts and relative therapy are triggered on the basis of prediction, obtained with our NN algorithm, instead of CGM. Finally, possible inclusion of additional pieces of information such as physical activity is investigated, though at a preliminary level. The thesis is organized as follows. Chapter 1 gives an introduction to the diabetes disease and the current technologies for CGM, presents state of art techniques for short-time prediction of glucose concentration of diabetics and states the aim and the novelty of the thesis. Chapter 2 discusses NN paradigms from a theoretical point of view and specifies technical details common to the design and implementation of all the NN algorithms proposed in the following. Chapter 3 describes the first prediction model we propose, based on a NN in parallel with a linear algorithm. Chapter 4 presents an alternative simpler architecture, based on a jump NN, and demonstrates its equivalence, in terms of performance, with the previously proposed algorithm. Chapter 5 further improves the jump NN, by adding new inputs and investigating their effective utility by a sensitivity analysis. Chapter 6 points out possible future developments, as the possibility of exploiting information on physical activity, reporting also a preliminary analysis. Finally, Chapter 7 describes the application of NN for generation of preventive hypoglycemic alerts and evaluates improvement of diabetes management in a simulated environment. Some concluding remarks end the thesis