Hybrid PSO-based variable translation wavelet neural network and its application to hypoglycemia detection system

To provide the detection of hypoglycemic episodes in Type 1 diabetes mellitus, hypoglycemia detection system is developed by the use of variable translation wavelet neural network (VTWNN) in this paper. A wavelet neural network with variable translation parameter is selected as a suitable classifier because of its excellent characteristics in capturing nonstationary signal analysis and nonlinear function modeling. Due to the variable translation parameters, the network becomes an adaptive network and provides better classification performance. An improved hybrid particle swarm optimization is used to train the parameters of VTWNN. Using the proposed classifier, a sensitivity of 81.40 % and a specificity of 50.91 % were achieved. The comparison results also show that the proposed detection system performs well in terms of good sensitivity and acceptable specificity. © 2012 Springer-Verlag London Limited

Classification of hypoglycemic episodes for Type 1 diabetes mellitus based on neural networks

Hypoglycemia is dangerous for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, we have developed a classification unit with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed classification unit is built and is validated by using the real T1DM patients' data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based classification unit can achieve more accurate results on both trained and unseen T1DM patients' data sets compared with those developed based on the commonly used classification methods for medical diagnosis including statistical regression, fuzzy regression and genetic programming

EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

Proposing Adaptive Insulin Dosage For Type 1 Diabetes By Fuzzy-based Controller

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2013Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2013Bu çalışmada, Tip 1 diyabet hastaları için insülin dozları öneren ve kan şekeri düzeylerindeki değişikliklere uyum sağlayabilen kapalı döngü bir kontrol sistemi önerilmektedir. Diğer çalışmalardan farklı olarak, önerilen bu sistem, hastaya ait, ard arda alınmış üç kan şekeri ölçüm değerini karşılaştırarak, hastaya kan şekeri düzeyini normal sınırlarda tutmaya yardımcı olacak insülin dozlarını hesaplar. Önerilen bu sistem gönüllü gerçek bir Tip 1 diyabet hastası üzerinde de test edilmiştir. Böylelikle hastanın normal tedavisinde kullandığı insülin miktarları ile sistem tarafından önerilen insülin miktarları karşılaştırılmıştır. Buna ek olarak, sistem farklı kan şekeri profilleri üzerinde denenerek, değişime uyum sağladığı ve tutarlı sonuçlar ürettiği gözlemlenmiştir. Böyle bir kontrol sisteminin tasarımı için en yaygın yapay zekâ tekniklerinden olan Bulanık Mantık tekniği kullanılmıştır. Bu amaçla, tasarlanan kontrol sistemi FIS editörü kullanılarak, MATLAB programlama dili ortamında gerçekleştirilmiştir.Emre ÇANAYAZ This study presents an adaptive closed loop system that proposes insulin dosage to regulate the blood glucose level of Type 1 diabetes patients. Developed prototype system contains of two Mamdani–type fuzzy logic controllers, database files with patient information, response of the system is stored, and insulin on board module prevents the user from overlapping injections. One fuzzy controller adjusts the amount of insulin by producing a coefficient factor according to the age and body mass index of the patient, another one purposes insulin doses comparing the patient’s glucose readings. In this prototype, Centroid method, which is the most common method used in fuzzy logic systems, was used for defuzzyfication process. Unlike prior studies, our proposed system compares three arbitrary blood glucose readings and proposes an insulin dosage to regulate blood glucose level of the patient. The system was tested using blood glucose readings of a real type 1 diabetes patient. Additionally, a second type-1 diabetes mellitus patient was used to test the system stability and adaptive abilities. Moreover, generated insulin dosages were compared with the multiple daily injection treatment dosage to prove system’s reliability. Finally, MATLAB programming environment and fuzzy logic toolbox were used to design the system.Yüksek LisansM.Sc

STOCHASTIC SEASONAL MODELS FOR GLUCOSE PREDICTION IN TYPE 1 DIABETES

[ES] La diabetes es un importante problema de salud mundial, siendo una de las enfermedades no transmisibles más graves después de las enfermedades cardiovasculares, el cáncer y las enfermedades respiratorias crónicas. La prevalencia de la diabetes ha aumentado constantemente en las últimas décadas, especialmente en países de ingresos bajos y medios. Se estima que 425 millones de personas en todo el mundo tenían diabetes en 2017, y para 2045 este número puede aumentar a 629 millones. Alrededor del 10% de las personas con diabetes padecen diabetes tipo 1, caracterizada por una destrucción autoinmune de las células beta en el páncreas, responsables de la secreción de la hormona insulina. Sin insulina, la glucosa plasmática aumenta a niveles nocivos, provocando complicaciones vasculares a largo plazo. Hasta que se encuentre una cura, el manejo de la diabetes depende de los avances tecnológicos para terapias de reemplazo de insulina. Con la llegada de los monitores continuos de glucosa, la tecnología ha evolucionado hacia sistemas automatizados. Acuñados como "páncreas artificial", los dispositivos de control de glucosa en lazo cerrado suponen hoy en día un cambio de juego en el manejo de la diabetes. La investigación en las últimas décadas ha sido intensa, dando lugar al primer sistema comercial a fines de 2017, y muchos más están siendo desarrollados por las principales industrias de dispositivos médicos. Sin embargo, como dispositivo de primera generación, muchos problemas aún permanecen abiertos y nuevos avances tecnológicos conducirán a mejoras del sistema para obtener mejores resultados de control glucémico y reducir la carga del paciente, mejorando significativamente la calidad de vida de las personas con diabetes tipo 1. En el centro de cualquier sistema de páncreas artificial se encuentra la predicción de glucosa, tema abordado en esta tesis. La capacidad de predecir la glucosa a lo largo de un horizonte de predicción dado, y la estimación de las tendencias futuras de glucosa, es la característica más importante de cualquier sistema de páncreas artificial, para poder tomar medidas preventivas que eviten por completo el riesgo para el paciente. La predicción de glucosa puede aparecer como parte del algoritmo de control en sí, como en sistemas basados en técnicas de control predictivo basado en modelo (MPC), o como parte de un sistema de supervisión para evitar episodios de hipoglucemia. Sin embargo, predecir la glucosa es un problema muy desafiante debido a la gran variabilidad inter e intra-sujeto que sufren los pacientes, cuyas fuentes solo se entienden parcialmente. Esto limita las prestaciones predictivas de los modelos, imponiendo horizontes de predicción relativamente cortos, independientemente de la técnica de modelado utilizada (modelos fisiológicos, basados en datos o híbridos). La hipótesis de partida de esta tesis es que la complejidad de la dinámica de la glucosa requiere la capacidad de caracterizar grupos de comportamientos en los datos históricos del paciente que llevan naturalmente al concepto de modelado local. Además, la similitud de las respuestas en un grupo puede aprovecharse aún más para introducir el concepto clásico de estacionalidad en la predicción de glucosa. Como resultado, los modelos locales estacionales están en el centro de esta tesis. Se utilizan varias bases de datos clínicas que incluyen comidas mixtas y ejercicio para demostrar la viabilidad y superioridad de las prestaciones de este enfoque.[CA] La diabetisés un important problema de salut mundial, sent una de les malalties no transmissibles més greus després de les malalties cardiovasculars, el càncer i les malalties respiratòries cròniques. La prevalença de la diabetis ha augmentat constantment en les últimes dècades, especialment en països d'ingressos baixos i mitjans. S'estima que 425 milions de persones a tot el món tenien diabetis en 2017, i per 2045 aquest nombre pot augmentar a 629 milions. Al voltant del 10% de les persones amb diabetis pateixen diabetis tipus 1, caracteritzada per una destrucció autoimmune de les cèl·lules beta en el pàncrees, responsables de la secreció de l'hormona insulina. Sense insulina, la glucosa plasmàtica augmenta a nivells nocius, provocant complicacions vasculars a llarg termini. Fins que es trobi una cura, el maneig de la diabetis depén dels avenços tecnològics per a teràpies de reemplaçament d'insulina. Amb l'arribada dels monitors continus de glucosa, la tecnologia ha evolucionat cap a sistemes automatitzats. Encunyats com "pàncrees artificial", els dispositius de control de glucosa en llaç tancat suposen avui dia un canvi de joc en el maneig de la diabetis. La investigació en les últimes dècades ha estat intensa, donant lloc al primer sistema comercial a finals de 2017, i molts més estan sent desenvolupats per les principals indústries de dispositius mèdics. No obstant això, com a dispositiu de primera generació, molts problemes encara romanen oberts i nous avenços tecnològics conduiran a millores del sistema per obtenir millors resultats de control glucèmic i reduir la càrrega del pacient, millorant significativament la qualitat de vida de les persones amb diabetis tipus 1. Al centre de qualsevol sistema de pàncrees artificial es troba la predicció de glucosa, tema abordat en aquesta tesi. La capacitat de predir la glucosa al llarg d'un horitzó de predicció donat, i l'estimació de les tendències futures de glucosa, és la característica més important de qualsevol sistema de pàncrees artificial, per poder prendre mesures preventives que evitin completament el risc per el pacient. La predicció de glucosa pot aparèixer com a part de l'algoritme de control en si, com en sistemes basats en técniques de control predictiu basat en model (MPC), o com a part d'un sistema de supervisió per evitar episodis d'hipoglucèmia. No obstant això, predir la glucosa és un problema molt desafiant degut a la gran variabilitat inter i intra-subjecte que pateixen els pacients, les fonts només s'entenen parcialment. Això limita les prestacions predictives dels models, imposant horitzons de predicció relativament curts, independentment de la tècnica de modelatge utilitzada (models fisiològics, basats en dades o híbrids). La hipòtesi de partida d'aquesta tesi és que la complexitat de la dinàmica de la glucosa requereix la capacitat de caracteritzar grups de comportaments en les dades històriques del pacient que porten naturalment al concepte de modelatge local. A més, la similitud de les respostes en un grup pot aprofitar-se encara més per introduir el concepte clàssic d'estacionalitat en la predicció de glucosa. Com a resultat, els models locals estacionals estan al centre d'aquesta tesi. S'utilitzen diverses bases de dades clíniques que inclouen menjars mixtes i exercici per demostrar la viabilitat i superioritat de les prestacions d'aquest enfocament.[EN] Diabetes is a significant global health problem, one of the most serious noncommunicable diseases after cardiovascular diseases, cancer and chronic respiratory diseases. Diabetes prevalence has been steadily increasing over the past decades, especially in low- and middle-income countries. It is estimated that 425 million people worldwide had diabetes in 2017, and by 2045 this number may rise to 629 million. About 10% of people with diabetes suffer from type 1 diabetes, characterized by autoimmune destruction of the beta-cells in the pancreas, responsible for the secretion of the hormone insulin. Without insulin, plasma glucose rises to deleterious levels, provoking long-term vascular complications. Until a cure is found, the management of diabetes relies on technological developments for insulin replacement therapies. With the advent of continuous glucose monitors, technology has been evolving towards automated systems. Coined as "artificial pancreas", closed-loop glucose control devices are nowadays a game-changer in diabetes management. Research in the last decades has been intense, yielding a first commercial system in late 2017 and many more are in the pipeline of the main medical devices industry. However, as a first-generation device, many issues still remain open and new technological advancements will lead to system improvements for better glycemic control outputs and reduced patient's burden, improving significantly the quality of life of people with type 1 diabetes. At the core of any artificial pancreas system is glucose prediction, the topic addressed in this thesis. The ability to predict glucose along a given prediction horizon, and estimation of future glucose trends, is the most important feature of any artificial pancreas system, in order to be able to take preventive actions to entirely avoid risk to the patient. Glucose prediction can appear as part of the control algorithm itself, such as in systems based on model predictive control (MPC) techniques, or as part of a monitoring system to avoid hypoglycemic episodes. However, predicting glucose is a very challenging problem due to the large inter- and intra-subject variability that patients suffer, whose sources are only partially understood. These limits models forecasting performance, imposing relatively short prediction horizons, despite the modeling technique used (physiological, data-driven or hybrid approaches). The starting hypothesis of this thesis is that the complexity of glucose dynamics requires the ability to characterize clusters of behaviors in the patient's historical data naturally yielding to the concept of local modeling. Besides, the similarity of responses in a cluster can be further exploited to introduce the classical concept of seasonality into glucose prediction. As a result, seasonal local models are at the core of this thesis. Several clinical databases including mixed meals and exercise are used to demonstrate the feasibility and superiority of the performance of this approach.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under the FPI grant BES-2014-069253 and projects DPI2013-46982-C2-1-R and DPI2016-78831-C2-1-R. Moreover, with relation to this grant, a short stay was done at the end of 2017 at the Illinois Institute of Technology, Chicago, United States of America, under the supervision of Prof. Ali Cinar, for four months from 01/09/2017 to 29/12/2017.Montaser Roushdi Ali, E. (2020). STOCHASTIC SEASONAL MODELS FOR GLUCOSE PREDICTION IN TYPE 1 DIABETES [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/136574TESI

Clinical evaluation of a novel adaptive bolus calculator and safety system in Type 1 diabetes

Bolus calculators are considered state-of-the-art for insulin dosing decision support for people with Type 1 diabetes (T1D). However, they all lack the ability to automatically adapt in real-time to respond to an individual’s needs or changes in insulin sensitivity. A novel insulin recommender system based on artificial intelligence has been developed to provide personalised bolus advice, namely the Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system. Besides adaptive bolus advice, the decision support system is coupled with a safety system which includes alarms, predictive glucose alerts, predictive low glucose suspend for insulin pump users, personalised carbohydrate recommendations and dynamic bolus insulin constraint. This thesis outlines the clinical evaluation of the PEPPER system in adults with T1D on multiple daily injections (MDI) and insulin pump therapy. The hypothesis was that the PEPPER system is safe, feasible and effective for use in people with TID using MDI or pump therapy. Safety and feasibility of the safety system was initially evaluated in the first phase, with the second phase evaluating feasibility of the complete system (safety system and adaptive bolus advisor). Finally, the whole system was clinically evaluated in a randomised crossover trial with 58 participants. No significant differences were observed for percentage times in range between the PEPPER and Control groups. For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. Overall, the studies demonstrated that the PEPPER system is safe and feasible for use when compared to conventional therapy (continuous glucose monitoring and standard bolus calculator). Further studies are required to confirm overall effectiveness.Open Acces

Complex event recognition through wearable sensors

Complex events are instrumental in understanding advanced behaviours and properties of a system. They can represent more meaningful events as compared to simple events. In this thesis we propose to use wearable sensor signals to detect complex events. These signals are pertaining to the user's state and therefore allow us to understand advanced characteristics about her. We propose a hierarchical approach to detect simple events from the wearable sensors data and then build complex events on top of them. In order to address privacy concerns that rise from the use of sensitive signals, we propose to perform all the computation on device. While this ensures the privacy of the data, it poses the problem of having limited computational resources. This problem is tackled by introducing energy efficient approaches based on incremental algorithms. A second challenge is the multiple levels of noise in the process. A first level of noise concerns the raw signals that are inherently imprecise (e.g. inaccuracy in GPS readings). A second level of noise, that we call semantic noise, is present among the simple events detected. Some of these simple events can disturb the detection of complex events effectively acting as noise. We apply the hierarchical approach in two different contexts defining the two different parts of our thesis. In the first part, we present a mobile system that builds a representation of the user's life. This system is based on the episodic memory model, which is responsible for the storage and recollection of past experiences. Following the hierarchical approach, the system processes raw signals to detect simple events such as places where the user stayed a certain amount of time to perform an activity, therefore building sequences of detected activities. These activities are in turn processed to detect complex events that we call routines and that represent recurrent patterns in the life of the user. In the second part of this thesis, we focus on the detection of glycemic events for diabetes type-1 patients in a non-invasive manner. Diabetics are not able to properly regulate their glucose, leading to periods of high and low blood sugar. We leverage signals (Electrocardiogram (ECG), accelerometer, breathing rate) from a sport belt to infer such glycemic events. We propose a physiological model based on the variations of the ECG when the patient has low blood sugar, and an energy-based model that computes the current glucose level of the user based on her glucose intake, insulin intake and glucose consumption via physical activity. For both contexts, we evaluate our systems in term of accuracy by assessing wether the detected routines are meaningful, and wether the glycemic events are correctly detected, and in term of mobile performance, which confirms the fitness of our approaches for mobile computation

Ensemble-based Supervised Learning for Predicting Diabetes Onset

The research presented in this thesis aims to address the issue of undiagnosed diabetes cases. The current state of knowledge is that one in seventy people in the United Kingdom are living with undiagnosed diabetes, and only one in a hundred people could identify the main signs of diabetes. Some of the tools available for predicting diabetes are either too simplistic and/or rely on superficial data for inference. On the positive side, the National Health Service (NHS) are improving data recording in this domain by offering health check to adults aged 40 - 70. Data from such programme could be utilised to mitigate the issue of superficial data; but also help to develop a predictive tool that facilitates a change from the current reactive care, onto one that is proactive. This thesis presents a tool based on a machine learning ensemble for predicting diabetes onset. Ensembles often perform better than a single classifier, and accuracy and diversity have been highlighted as the two vital requirements for constructing good ensemble classifiers. Experiments in this thesis explore the relationship between diversity from heterogeneous ensemble classifiers and the accuracy of predictions through feature subset selection in order to predict diabetes onset. Data from a national health check programme (similar to NHS health check) was used. The aim is to predict diabetes onset better than other similar studies within the literature. For the experiments, predictions from five base classifiers (Sequential Minimal Optimisation (SMO), Radial Basis Function (RBF), Naïve Bayes (NB), Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and C4.5 decision tree), performing the same task, are exploited in all possible combinations to construct 26 ensemble models. The training data feature space was searched to select the best feature subset for each classifier. Selected subsets are used to train the classifiers and their predictions are combined using k-Nearest Neighbours algorithm as meta-classifier. Results are analysed using four performance metrics (accuracy, sensitivity, specificity and AUC) to determine (i) if ensembles always perform better than single classifier; and (ii) the impact of diversity (from heterogeneous classifiers) and accuracy (through feature subset selection) on ensemble performance. At base classification level, RBF produced better results than the other four classifiers with 78%accuracy, 82% sensitivity, 73% specificity and 85% AUC. A comparative study shows that RBF model is more accurate than 9 ensembles, more sensitive than 13 ensembles, more specific than 9 ensembles; and produced better AUC than 25 ensembles. This means that ensembles do not always perform better than its constituent classifiers. Of those ensembles that performed better than RBF, the combination of C4.5, RIPPER and NB produced the highest results with 83% accuracy, 87% sensitivity, 79% specificity, and 86% AUC. When compared to the RBF model, the result shows 5.37% accuracy improvement which is significant (p = 0.0332). The experiments show how data from medical health examination can be utilised to address the issue of undiagnosed cases of diabetes. Models constructed with such data would facilitate the much desired shift from preventive to proactive care for individuals at high risk of diabetes. From the machine learning view point, it was established that ensembles constructed based on diverse and accurate base learners, have the potential to produce significant improvement in accuracy, compared to its individual constituent classifiers. In addition, the ensemble presented in this thesis is at least 1% and at most 23% more accurate than similar research studies found within the literature. This validates the superiority of the method implemented

Faculty Publications and Creative Works 2001

One of the ways in which we recognize our faculty at the University of New Mexico is through Faculty Publications & Creative Works. An annual publication, it highlights our faculty\u27s scholarly and creative activities and achievements and serves as a compendium of UNM faculty efforts during the 2001 calendar year. Faculty Publications & Creative Works strives to illustrate the depth and breadth of research activities performed throughout our University\u27s laboratories, studios and classrooms. We believe that the communication of individual research is a significant method of sharing concepts and thoughts and ultimately inspiring the birth of new ideas. In support of this, UNM faculty during 2001 produced over 2,299* works, including 1,685 scholarly papers and articles, 69 books, 269 book chapters, 184 reviews, 86 creative works and 6 patented works. We are proud of the accomplishments of our faculty which are in part reflected in this book, which illustrates the diversity of intellectual pursuits in support of research and education at the University of New Mexico