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Zecchin, Chiara (2014) Online Glucose Prediction in Type-1 Diabetes by Neural Network Models. [Tesi di dottorato]

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Abstract (inglese)

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.

Abstract (italiano)

Il diabete mellito è una patologia cronica caratterizzata da disfunzioni della regolazione della concentrazione di glucosio nel sangue. Nel diabete di Tipo 1 il pancreas non produce l'ormone insulina, mentre nel diabete di Tipo 2 si verificano squilibri nella secrezione e nell'azione dell'insulina. Di conseguenza, spesso la concentrazione glicemica eccede le soglie di normalità (70-180 mg/dL), con complicazioni a breve e lungo termine. L'ipoglicemia (glicemia inferiore a 70 mg/dL) può risultare in alterazione delle capacità cognitive, cambiamenti d'umore, convulsioni e coma. L'iperglicemia (glicemia superiore a 180 mg/dL) predispone, nel lungo termine, a patologie invalidanti, come neuropatie, nefropatie, retinopatie e piede diabetico. L'obiettivo della terapia convenzionale del diabete è il mantenimento della glicemia nell'intervallo di normalità regolando la dieta, la terapia insulinica e l'esercizio fisico in base a 4-5 monitoraggi giornalieri della glicemia, (Self-Monitoring of Blood Glucose, SMBG), effettuati dal paziente stesso usando un dispositivo pungidito, portabile e minimamente invasivo. Negli ultimi 15 anni si sono aperti nuovi orizzonti nel trattamento del diabete, grazie all'introduzione, nella ricerca clinica, di sensori minimamente invasivi (Continuous Glucose Monitoring, CGM) capaci di misurare la glicemia nel sottocute in modo quasi continuo (ovvero con una misurazione ogni 1-5 min) per parecchi giorni consecutivi (dai 7 ai 10 giorni). I sensori CGM permettono di monitorare le dinamiche glicemiche in modo più fine delle misurazioni SMBG e le serie temporali di concentrazione glicemica possono essere utilizzate sia retrospettivamente, per esempio per ottimizzare la terapia di controllo metabolico, sia prospettivamente in tempo reale, per esempio per generare segnali di allarme quando la concentrazione glicemica oltrepassa le soglie di normalità o nel “pancreas artificiale”. Per quanto concerne le applicazioni in tempo reale, poter prevenire gli eventi critici sarebbe chiaramente più attraente che semplicemente individuarli, contestualmente al loro verificarsi. Ciò sarebbe fattibile se si conoscesse la concentrazione glicemia futura con circa 30-45 min di anticipo. La natura quasi continua del segnale CGM rende possibile l'uso di algoritmi predittivi che possono, potenzialmente, permettere ai pazienti diabetici di ottimizzare le decisioni terapeutiche sulla base della glicemia futura, invece che attuale, dando loro l'opportunità di limitare l'impatto di eventi pericolosi per la salute, se non di evitarli. Dopo l'introduzione nella pratica clinica dei dispositivi CGM, in letteratura, sono stati proposti vari metodi per la predizione a breve termine della glicemia. Si tratta principalmente di algoritmi basati su modelli di serie temporali e la maggior parte di essi utilizza solamente la storia del segnale CGM come ingresso. Tuttavia, le dinamiche glicemiche sono determinate da molti fattori, come la quantità di carboidrati ingeriti durante i pasti, la somministrazione di farmaci, compresa l'insulina, l'attività fisica, lo stress, le emozioni. Inoltre, la variabilità inter- e intra- individuale è elevata. Per questi motivi, predire l'andamento glicemico futuro è difficile e stimolante e c'è margine di miglioramento dei risultati pubblicati finora in letteratura.
Lo scopo di questa tesi è investigare la possibilità di predire la concentrazione glicemica futura, nel breve termine, utilizzando modelli basati su reti neurali (Neural Network, NN) e sfruttando, oltre alla storia del segnale CGM, altre informazioni disponibili. Nel dettaglio, inizialmente svilupperemo un nuovo modello che utilizza, come ingressi, il segnale CGM e informazioni relative ai pasti ingeriti, (istante temporale e quantità di carboidrati). L'algoritmo predittivo sarà basato su una NN di tipo feedforward, in parallelo ad un modello lineare. I risultati sono promettenti: il modello è superiore ad algoritmi stato dell'arte ampiamente utilizzati, la predizione è accurata e il guadagno temporale è soddisfacente. Successivamente proporremo un nuovo modello basato su una differente architettura di NN, ovvero una “jump NN”, che fonde i benefici di una NN di tipo feedforward e di un algoritmo lineare, ottenendo risultati simili a quelli del modello precedentemente proposto, nonostante la sua struttura notevolmente più semplice. Per completare l'analisi, valuteremo l'inclusione, tra gli ingressi della jump NN, di segnali ottenuti sfruttando informazioni sulla terapia insulinica (istante temporale e dose dei boli iniettati) e valuteremo l'importanza e l'influenza relativa di ogni ingresso nella determinazione del valore glicemico predetto dalla NN, sviluppando un'originale analisi di sensitività. Tutti i modelli proposti saranno valutati su dati reali di pazienti diabetici di Tipo 1, raccolti durante il progetto Europeo FP7 (7th Framework Programme, Settimo Programma Quadro) DIAdvisor. Per valutare l'utilità clinica della predizione e il miglioramento della gestione della terapia diabetica proporremo una nuova strategia per la quantificazione, in simulazione, della riduzione del numero e della gravità degli eventi ipoglicemici nel caso gli allarmi, e la relativa terapia, siano determinati sulla base della concentrazione glicemica predetta, utilizzando il nostro algoritmo basato su NN, invece che su quella misurata dal sensore CGM. Infine, investigheremo, in modo preliminare, la possibilità di includere, tra gli ingressi della NN, ulteriori informazioni, come l'attività fisica.
La tesi è organizzata come descritto in seguito. Il Capitolo 1 introduce la patologia diabetica e le attuali tecnologie CGM, presenta le tecniche stato dell'arte utilizzate per la predizione a breve termine della glicemia di pazienti diabetici e specifica gli scopi e le innovazioni della presente tesi. Il Capitolo 2 introduce le basi teoriche delle NN e specifica i dettagli tecnici che abbiamo scelto di adottare per lo sviluppo e l'implementazione di tutte le NN proposte in seguito. Il Capitolo 3 descrive il primo modello proposto, basato su una NN in parallelo a un algoritmo lineare. Il Capitolo 4 presenta una struttura alternativa più semplice, basata su una jump NN, e dimostra la sua equivalenza, in termini di prestazioni, con il modello precedentemente proposto. Il Capitolo 5 apporta ulteriori miglioramenti alla jump NN, aggiungendo nuovi ingressi e investigando la loro utilità effettiva attraverso un'analisi di sensitività. Il Capitolo 6 indica possibili sviluppi futuri, come l'inclusione di informazioni sull'attività fisica, presentando anche un'analisi preliminare. Infine, il Capitolo 7 applica la NN per la generazione di allarmi preventivi per l'ipoglicemia, valutando, in simulazione, il miglioramento della gestione del diabete. Alcuni commenti e osservazioni concludono la tesi.

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Tipo di EPrint:Tesi di dottorato
Relatore:Sparacino, Giovanni
Dottorato (corsi e scuole):Ciclo 26 > Scuole 26 > INGEGNERIA DELL'INFORMAZIONE > BIOINGEGNERIA
Data di deposito della tesi:27 Gennaio 2014
Anno di Pubblicazione:27 Gennaio 2014
Parole chiave (italiano / inglese):monitoraggio continuo della glicemia, analisi nonlineare del segnale; predizione; glicemia; modellistica non lineari; diabete di tipo 1; rete neurale; glucosio; sensore CGM; Continuous Glucose Monitoring; nonlinear signal processing; forecast; glycemia; nonlinear modelling; type 1 diabetes; neural network; glucose; prediction; CGM sensor
Settori scientifico-disciplinari MIUR:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/06 Bioingegneria elettronica e informatica
Struttura di riferimento:Dipartimenti > Dipartimento di Ingegneria dell'Informazione
Codice ID:6418
Depositato il:31 Ott 2014 13:23
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