Non-Invasive Continuous Glucose Monitoring: Identification of Models for Multi-Sensor Systems

Diabetes is a disease that undermines the normal regulation of glucose levels in the blood. In people with diabetes, the body does not secrete insulin (Type 1 diabetes) or derangements occur in both insulin secretion and action (Type 2 diabetes). In spite of the therapy, which is mainly based on controlled regimens of insulin and drug administration, diet, and physical exercise, tuned according to self-monitoring of blood glucose (SMBG) levels 3-4 times a day, blood glucose concentration often exceeds the normal range thresholds of 70-180 mg/dL. While hyperglycaemia mostly affects long-term complications (such as neuropathy, retinopathy, cardiovascular, and heart diseases), hypoglycaemia can be very dangerous in the short-term and, in the worst-case scenario, may bring the patient into hypoglycaemic coma. New scenarios in diabetes treatment have been opened in the last 15 years, when continuous glucose monitoring (CGM) sensors, able to monitor glucose concentration continuously (i.e. with a reading every 1 to 5 min) over several days, entered clinical research. CGM sensors can be used both retrospectively, e.g., to optimize the metabolic control, and in real-time applications, e.g., in the "smart" CGM sensors, able to generate alerts when glucose concentrations are predicted to exceed the normal range thresholds or in the so-called "artificial pancreas". Most CGM sensors exploit needles and are thus invasive, although minimally. In order to improve patients comfort, Non-Invasive Continuous Glucose Monitoring (NI-CGM) technologies have been widely investigated in the last years and their ability to monitor glucose changes in the human body has been demonstrated under highly controlled (e.g. in-clinic) conditions. As soon as these conditions become less favourable (e.g. in daily-life use) several problems have been experienced that can be associated with physiological and environmental perturbations. To tackle this issue, the multisensor concept received greater attention in the last few years. A multisensor consists in the embedding of sensors of different nature within the same device, allowing the measurement of endogenous (glucose, skin perfusion, sweating, movement, etc.) as well as exogenous (temperature, humidity, etc.) factors. The main glucose related signals and those measuring specific detrimental processes have to be combined through a suitable mathematical model with the final goal of estimating glucose non-invasively. White-box models, where differential equations are used to describe the internal behavior of the system, can be rarely considered to combine multisensor measurements because a physical/mechanistic model linking multisensor data to glucose is not easily available. A more viable approach considers black-box models, which do not describe the internal mechanisms of the system under study, but rather depict how the inputs (channels from the non-invasive device) determine the output (estimated glucose values) through a transfer function (which we restrict to the class of multivariate linear models). Unfortunately, numerical problems usually arise in the identication of model parameters, since the multisensor channels are highly correlated (especially for spectroscopy based devices) and for the potentially high dimension of the measurement space. The aim of the thesis is to investigate and evaluate different techniques usable for the identication of the multivariate linear regression models parameters linking multisensor data and glucose. In particular, the following methods are considered: Ordinary Least Squares (OLS); Partial Least Squares (PLS); the Least Absolute Shrinkage and Selection Operator (LASSO) based on l1 norm regularization; Ridge regression based on l2 norm regularization; Elastic Net (EN), based on the combination of the two previous norms. As a case study, we consider data from the Multisensor device mainly based on dielectric and optical sensors developed by Solianis Monitoring AG (Zurich, Switzerland) which partially sponsored the PhD scholarship. Solianis Monitoring AG IP portfolio is now held by Biovotion AG (Zurich, Switzerland). Forty-five recording sessions provided by Solianis Monitoring AG and collected in 6 diabetic human beings undertaken hypo and hyperglycaemic protocols performed at the University Hospital Zurich are considered. The models identified with the aforementioned techniques using a data subset are then assessed against an independent test data subset. Results show that methods controlling complexity outperform OLS during model test. In general, regularization techniques outperform PLS, especially those embedding the l1 norm (LASSO end EN), because they set many channel weights to zero thus resulting more robust to occasional spikes occurring in the Multisensor channels. In particular, the EN model results the best one, sharing both the properties of sparseness and the grouping effect induced by the l1 and l2 norms respectively. In general, results indicate that, although the performance, in terms of overall accuracy, is not yet comparable with that of SMBG enzyme-based needle sensors, the Multisensor platform combined with the Elastic-Net (EN) models is a valid tool for the real-time monitoring of glycaemic trends. An effective application concerns the complement of sparse SMBG measures with glucose trend information within the recently developed concept of dynamic risk for the correct judgment of dangerous events such as hypoglycaemia. The body of the thesis is organized into three main parts: Part I (including Chapters 1 to 4), first gives an introduction of the diabetes disease and of the current technologies for NI-CGM (including the Multisensor device by Solianis) and then states the aims of the thesis; Part II (which includes Chapters 5 to 9), first describes some of the issues to be faced in high dimensional regression problems, and then presents OLS, PLS, LASSO, Ridge and EN using a tutorial example to highlight their advantages and drawbacks; Finally, Part III (including Chapters 10-12), presents the case study with the data set and results. Some concluding remarks and possible future developments end the thesis. In particular, a Monte Carlo procedure to evaluate robustness of the calibration procedure for the Solianis Multisensor device is proposed, together with a new cost function to be used for identifying models

Regularised Model Identification Improves Accuracy of Multisensor Systems for Noninvasive Continuous Glucose Monitoring in Diabetes Management

Continuous glucose monitoring (CGM) by suitable portable sensors plays a central role in the treatment of diabetes, a disease currently affecting more than 350 million people worldwide. Noninvasive CGM (NI-CGM), in particular, is appealing for reasons related to patient comfort (no needles are used) but challenging. NI-CGM prototypes exploiting multisensor approaches have been recently proposed to deal with physiological and environmental disturbances. In these prototypes, signals measured noninvasively (e.g., skin impedance, temperature, optical skin properties, etc.) are combined through a static multivariate linear model for estimating glucose levels. In this work, by exploiting a dataset of 45 experimental sessions acquired in diabetic subjects, we show that regularisation-based techniques for the identification of the model, such as the least absolute shrinkage and selection operator (better known as LASSO), Ridge regression, and Elastic-Net regression, improve the accuracy of glucose estimates with respect to techniques, such as partial least squares regression, previously used in the literature. More specifically, the Elastic-Net model (i.e., the model identified using a combination of ${l}_{1}$ and ${l}_{2}$ norms) has the best results, according to the metrics widely accepted in the diabetes community. This model represents an important incremental step toward the development of NI-CGM devices effectively usable by patients

Alterations in resting-state functional connectivity after brain posterior lesions reflect the functionality of the visual system in hemianopic patients

Emerging evidence suggests a role of the posterior cortices in regulating alpha oscillatory activity and organizing low-level processing in non-alpha frequency bands. Therefore, posterior brain lesions, which damage the neural circuits of the visual system, might affect functional connectivity patterns of brain rhythms. To test this hypothesis, eyes-closed resting state EEG signal was acquired from patients with hemianopia with left and right posterior lesions, patients without hemianopia with more anterior lesions and healthy controls. Left-lesioned hemianopics showed reduced intrahemispheric connectivity in the range of upper alpha only in the lesioned hemisphere, whereas right-lesioned hemianopics exhibited reduced intrahemispheric alpha connectivity in both hemispheres. In terms of network topology, these impairments were characterized by reduced local functional segregation, with no associated change in global functional integration. This suggests a crucial role of posterior cortices in promoting functional connectivity in the range of alpha. Right-lesioned hemianopics revealed also additional impairments in the theta range, with increased connectivity in this frequency band, characterized by both increased local segregated activity and decreased global integration. This indicates that lesions to right posterior cortices lead to stronger impairments in alpha connectivity and induce additional alterations in local and global low-level processing, suggesting a specialization of the right hemisphere in generating alpha oscillations and in coordinating complex interplays with lower frequency bands. Importantly, hemianopic patient’s visual performance in the blind field was linked to alpha functional connectivity, corroborating the notion that alpha oscillatory patterns represent a biomarker of the integrity and the functioning of the underlying visual system.Fil: Gallina, Jessica. Universidad de Bologna; ItaliaFil: Zanon, Marco. Universidad de Bologna; ItaliaFil: Mikulan, Ezequiel Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Università degli Studi di Milano; ItaliaFil: Pietrelli, Mattia. Universidad de Bologna; ItaliaFil: Gambino, Silvia. Universidad de Bologna; ItaliaFil: Ibáñez, Santiago Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; ChileFil: Bertini, Caterina. Universidad de Bologna; Itali

An observational study to assess validity and reliability of smartphone sensor-based gait and balance assessments in multiple sclerosis: Floodlight GaitLab protocol

Background Gait and balance impairments are often present in people with multiple sclerosis (PwMS) and have a significant impact on quality of life and independence. Gold-standard quantitative tools for assessing gait and balance such as motion capture systems and force plates usually require complex technical setups. Wearable sensors, including those integrated into smartphones, offer a more frequent, convenient, and minimally burdensome assessment of functional disability in a home environment. We developed a novel smartphone sensor-based application (Floodlight) that is being used in multiple research and clinical contexts, but a complete validation of this technology is still lacking. Methods This protocol describes an observational study designed to evaluate the analytical and clinical validity of Floodlight gait and balance tests. Approximately 100 PwMS and 35 healthy controls will perform multiple gait and balance tasks in both laboratory-based and real-world environments in order to explore the following properties: (a) concurrent validity of the Floodlight gait and balance tests against gold-standard assessments; (b) reliability of Floodlight digital measures derived under different controlled gait and balance conditions, and different on-body sensor locations; (c) ecological validity of the tests; and (d) construct validity compared with clinician- and patient-reported assessments. Conclusions The Floodlight GaitLab study (ISRCTN15993728) represents a critical step in the technical validation of Floodlight technology to measure gait and balance in PwMS, and will also allow the development of new test designs and algorithms

Non-Invasive Continuous Glucose Monitoring: Identification of Models for Multi-Sensor Systems

Diabetes is a disease that undermines the normal regulation of glucose levels in the blood. In people with diabetes, the body does not secrete insulin (Type 1 diabetes) or derangements occur in both insulin secretion and action (Type 2 diabetes). In spite of the therapy, which is mainly based on controlled regimens of insulin and drug administration, diet, and physical exercise, tuned according to self-monitoring of blood glucose (SMBG) levels 3-4 times a day, blood glucose concentration often exceeds the normal range thresholds of 70-180 mg/dL. While hyperglycaemia mostly affects long-term complications (such as neuropathy, retinopathy, cardiovascular, and heart diseases), hypoglycaemia can be very dangerous in the short-term and, in the worst-case scenario, may bring the patient into hypoglycaemic coma. New scenarios in diabetes treatment have been opened in the last 15 years, when continuous glucose monitoring (CGM) sensors, able to monitor glucose concentration continuously (i.e. with a reading every 1 to 5 min) over several days, entered clinical research. CGM sensors can be used both retrospectively, e.g., to optimize the metabolic control, and in real-time applications, e.g., in the "smart" CGM sensors, able to generate alerts when glucose concentrations are predicted to exceed the normal range thresholds or in the so-called "artificial pancreas". Most CGM sensors exploit needles and are thus invasive, although minimally. In order to improve patients comfort, Non-Invasive Continuous Glucose Monitoring (NI-CGM) technologies have been widely investigated in the last years and their ability to monitor glucose changes in the human body has been demonstrated under highly controlled (e.g. in-clinic) conditions. As soon as these conditions become less favourable (e.g. in daily-life use) several problems have been experienced that can be associated with physiological and environmental perturbations. To tackle this issue, the multisensor concept received greater attention in the last few years. A multisensor consists in the embedding of sensors of different nature within the same device, allowing the measurement of endogenous (glucose, skin perfusion, sweating, movement, etc.) as well as exogenous (temperature, humidity, etc.) factors. The main glucose related signals and those measuring specific detrimental processes have to be combined through a suitable mathematical model with the final goal of estimating glucose non-invasively. White-box models, where differential equations are used to describe the internal behavior of the system, can be rarely considered to combine multisensor measurements because a physical/mechanistic model linking multisensor data to glucose is not easily available. A more viable approach considers black-box models, which do not describe the internal mechanisms of the system under study, but rather depict how the inputs (channels from the non-invasive device) determine the output (estimated glucose values) through a transfer function (which we restrict to the class of multivariate linear models). Unfortunately, numerical problems usually arise in the identication of model parameters, since the multisensor channels are highly correlated (especially for spectroscopy based devices) and for the potentially high dimension of the measurement space. The aim of the thesis is to investigate and evaluate different techniques usable for the identication of the multivariate linear regression models parameters linking multisensor data and glucose. In particular, the following methods are considered: Ordinary Least Squares (OLS); Partial Least Squares (PLS); the Least Absolute Shrinkage and Selection Operator (LASSO) based on l1 norm regularization; Ridge regression based on l2 norm regularization; Elastic Net (EN), based on the combination of the two previous norms. As a case study, we consider data from the Multisensor device mainly based on dielectric and optical sensors developed by Solianis Monitoring AG (Zurich, Switzerland) which partially sponsored the PhD scholarship. Solianis Monitoring AG IP portfolio is now held by Biovotion AG (Zurich, Switzerland). Forty-five recording sessions provided by Solianis Monitoring AG and collected in 6 diabetic human beings undertaken hypo and hyperglycaemic protocols performed at the University Hospital Zurich are considered. The models identified with the aforementioned techniques using a data subset are then assessed against an independent test data subset. Results show that methods controlling complexity outperform OLS during model test. In general, regularization techniques outperform PLS, especially those embedding the l1 norm (LASSO end EN), because they set many channel weights to zero thus resulting more robust to occasional spikes occurring in the Multisensor channels. In particular, the EN model results the best one, sharing both the properties of sparseness and the grouping effect induced by the l1 and l2 norms respectively. In general, results indicate that, although the performance, in terms of overall accuracy, is not yet comparable with that of SMBG enzyme-based needle sensors, the Multisensor platform combined with the Elastic-Net (EN) models is a valid tool for the real-time monitoring of glycaemic trends. An effective application concerns the complement of sparse SMBG measures with glucose trend information within the recently developed concept of dynamic risk for the correct judgment of dangerous events such as hypoglycaemia. The body of the thesis is organized into three main parts: Part I (including Chapters 1 to 4), first gives an introduction of the diabetes disease and of the current technologies for NI-CGM (including the Multisensor device by Solianis) and then states the aims of the thesis; Part II (which includes Chapters 5 to 9), first describes some of the issues to be faced in high dimensional regression problems, and then presents OLS, PLS, LASSO, Ridge and EN using a tutorial example to highlight their advantages and drawbacks; Finally, Part III (including Chapters 10-12), presents the case study with the data set and results. Some concluding remarks and possible future developments end the thesis. In particular, a Monte Carlo procedure to evaluate robustness of the calibration procedure for the Solianis Multisensor device is proposed, together with a new cost function to be used for identifying models.Il diabete e una malattia che compromette la normale regolazione dei livelli di glucosio nel sangue. Nelle persone diabetiche, il corpo non secerne insulina (diabete di tipo 1) o si vericano delle alterazioni sia nella secrezione che nell'azione dell'insulina stessa (diabete di tipo 2). La terapia si basa principalmente su somministrazione di insulina e farmaci, dieta ed esercizio fisico, modulati in base alla misurazione dei livelli di glucosio nel sangue 3-4 volte al giorno attraverso metodi finger-prick. Nonostante ciò, la concentrazione di glucosio nel sangue supera spesso le soglie di normalita di 70-180 mg/dL. Mentre l'iperglicemia implica complicanze a lungo termine (come ad esempio neuropatia, retinopatia, malattie cardiovascolari e cardiache), l'ipoglicemia puo essere molto pericolosa nel breve termine e, nel peggiore dei casi, portare il paziente in coma ipoglicemico. Nuovi scenari nella cura del diabete si sono affacciati negli ultimi 10 anni, quando sensori per il monitoraggio continuo della glucemia sono entrati nella fase di sperimentazione clinica. Questi sensori sono in grado di monitorare le concentrazioni di glucosio nel sangue con una lettura ogni 1-5 minuti per diversi giorni, permettendo un analisi sia retrospettiva, ad esempio per ottimizzare il controllo metabolico, che in tempo reale, per generare avvisi quando viene predetta l'uscita dalla normale banda euglicemica, e nel cosiddetto "pancreas artificiale". La maggior parte di questi sensori per il monitoraggio continuo della glicemia sono minimatmente invasivi perche sfruttano un piccolo ago inserito sottocute. Gli ultimi anni hanno visto un crescente interesse verso tecnologie non invasive per il monitoraggio continuo della glicemia, con l'obiettivo di migliorare il comfort del paziente. La loro capacità di monitorare i cambiamenti di glucosio nel corpo umano e stata dimostrata in condizioni altamente controllate tipiche di un'infrastruttura clinica. Non appena queste condizioni diventano meno favorevoli (ad esempio durante un uso quotidiano di queste tecnologie), sorgono diversi problemi associati a perturbazioni fisiologiche ed ambientali. Per affrontare questo problema, negli ultimi anni il concetto di "multisensore" ha ottenuto un crescente interesse. Esso consiste nell'integrazione di sensori di diversa natura all'interno dello stesso dispositivo, permettendo la misurazione di fattori endogeni (glucosio, perfusione del sangue, sudorazione, movimento, ecc) ed esogeni (temperatura, umidita, ecc). I segnali maggiormente correlati con il glucosio e quelli legati agli altri processi sono combinati con un opportuno modello matematico con l'obiettivo finale di stimare la glicemia in modo non invasivo. Modelli di sistema (o a "scatola bianca"), nei quali equazioni differenziali descrivono il comportamento interno del sistema, possono essere considerati raramente. Infatti, un modello fisico/meccanicistico legante i dati misurati dal multisensore con il glucosio non e facilmente disponibile. Un differente approccio vede l'impiego di modelli di dati (o a "scatola nera") che descrivono il sistema in esame in termini di ingressi (canali misurati dal dispositivo non invasivo), uscita (valori stimati di glucosio) e funzione di trasferimento (che in questa tesi si limita alla classe dei modelli di regressione lineari multivariati). In fase di identificazione dei parametri del modello potrebbero insorgere problemi numerici legati alla collinearita tra sottoinsiemi dei canali misurati dal multisensore (in particolare per i dispositivi basati su spettroscopia) e per la dimensione potenzialmente elevata dello spazio delle misure. L'obiettivo della tesi di dottorato e di investigare e valutare diverse tecniche per l'identicazione del modello di regressione lineare multivariata con lo scopo di stimare i livelli di glicemia non invasivamente. In particolare, i seguenti metodi sono considerati: Ordinary Least Squares (OLS), Partial Least Squares (PLS), the Least Absolute Shrinkage and Selection Operator (LASSO) basato sulla regolarizzazione con norma l1; Ridge basato sulla regolarizzazione con norma l2; Elastic-Net (EN) basato sulla combinazione delle due norme precedenti. Come caso di studio per l'applicazione delle metodologie proposte, consideriamo i dati misurati dal dispositivo multisensore, principalmente basato su sensori dielettrici ed ottici, sviluppato dall'azienda Solianis Monitoring AG (Zurigo, Svizzera), che ha parzialmente sostenuto gli oneri finanziari legati al progetto di dottorato durante il quale questa tesi e stata sviluppata. La tecnologia del multisensore e la proprietà intellettuale di Solianis sono ora detenute da Biovotion AG (Zurigo, Svizzera). Solianis Monitoring AG ha fornito quarantacinque sessioni sperimentali collezionate da 6 pazienti soggetti a protocolli ipo ed iperglicemici presso l'University Hospital Zurich. I modelli identificati con le tecniche di cui sopra, sono testati con un insieme di dati diverso da quello utilizzato per l'identicazione dei modelli stessi. I risultati dimostrano chei metodi di controllo della complessita hanno accuratezza maggiore rispetto ad OLS. In generale, le tecniche basate su regolarizzazione sono migliori rispetto a PLS. In particolare, quelle che sfruttano la norma l1 (LASSO ed EN), pongono molti coefficienti del modello a zero rendendo i profili stimati di glucosio piu robusti a rumore occasionale che interessa alcuni canali del multi-sensore. In particolare, il modello EN risulta il migliore, condividendo sia le proprietà di sparsita e l'effetto raggruppamento indotte rispettivamente dalle norme l1 ed l2. In generale, i risultati indicano che, anche se le prestazioni, in termini di accuratezza dei profili di glucosio stimati, non sono ancora confrontabili con quelle dei sensori basati su aghi, la piattaforma multisensore combinata con il modello EN è un valido strumento per il monitoraggio in tempo reale dei trend glicemici. Una possibile applicazione si basa sull'utilizzo del'informazione dei trend glicemici per completare misure rade effettuate con metodi finger-prick. Sfruttando il concetto di rischio dinamico recentemente sviluppato, e' possibile dare una corretta valutazione di eventi potenzialmente pericolosi come l'ipoglicemia. La tesi si articola in tre parti principali: Parte I (che comprende i Capitoli 1-4), fornisce inizialmente un'introduzione sul diabete, una recensione delle attuali tecnologie per il monitoraggio non-invasivo della glicemia (incluso il dispositivo multisensore di Solianis) e gli obiettivi della tesi; Parte II (che comprende i Capitoli 5-9), presenta alcune delle difficoltà affrontate quando si lavora con problemi di regressione su dati di grandi dimensioni, per poi presentare OLS, PLS, LASSO, Ridge e EN sfruttando un esempio tutorial per evidenziarne vantaggi e svantaggi. Infine, Parte III, (Capitoli 10-12) presenta il set di dati del caso di studio ed i risultati. Alcune note conclusive e possibili sviluppi futuri terminano la tesi. In particolare, vengono brevemente illustrate una metodologia basata su simulazioni Monte Carlo per valutare la robustezza della calibrazione del modello e l'utilizzo di un nuova nuova funzione obiettivo per l'identicazione dei modelli

Sparse Logistic Regression: Comparison of Regularization and Bayesian implementations

In knowledge-based systems, besides obtaining good output prediction accuracy, it is crucial to understand the subset of input variables that have most influence on the output, with the goal of gaining deeper insight into the underlying process. These requirements call for logistic model estimation techniques that provide a sparse solution, i.e., where coefficients associated with non-important variables are set to zero. In this work we compare the performance of two methods: the first one is based on the well known Least Absolute Shrinkage and Selection Operator (LASSO) which involves regularization with an l1 norm; the second one is the Relevance Vector Machine (RVM) which is based on a Bayesian implementation of the linear logistic model. The two methods are extensively compared in this paper, on real and simulated datasets. Results show that, in general, the two approaches are comparable in terms of prediction performance. RVM outperforms the LASSO both in term of structure recovery (estimation of the correct non-zero model coefficients) and prediction accuracy when the dimensionality of the data tends to increase. However, LASSO shows comparable performance to RVM when the dimensionality of the data is much higher than number of samples that is p >> n

Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device

Even if still at an early stage of development, non-invasive continuous glucose monitoring (NI-CGM) sensors represent a promising technology for optimizing diabetes therapy. Recent studies showed that the Multisensor provides useful information about glucose dynamics with a mean absolute relative difference (MARD) of 35.4% in a fully prospective setting. Here we propose a method that, exploiting the same Multisensor measurements, but in a retrospective setting, achieves a much better accuracy. Data acquired by the Multisensor during a long-term study are retrospectively processed following a two-step procedure. First, the raw data are transformed to a blood glucose (BG) estimate by a multiple linear regression model. Then, an enhancing module is applied in cascade to the regression model to improve the accuracy of the glucose estimation by retrofitting available BG references through a time-varying linear model. MARD between the retrospectively reconstructed BG time-series and reference values is 20%. Here, 94% of values fall in zone A or B of the Clarke Error Grid. The proposed algorithm achieved a level of accuracy that could make this device a potential complementary tool for diabetes management and also for guiding prediabetic or nondiabetic users through life-style changes

Assessment of linear regression techniques for modeling multisensor data for non-invasive continuous glucose monitoring.

New scenarios in diabetes treatment have been opened in the last ten years by continuous glucose monitoring (CGM) sensors. In particular, Non-Invasive CGM sensors are particularly appealing, even though they are still at an early stage of development. Solianis Monitoring AG (Zurich, Switzerland) has proposed an approach based on a multisensor concept, embedding primarily dielectric spectroscopy and optical sensors. This concept requires a mathematical model able to reconstruct the glucose concentration from the 150 channels measured with the device. Assuming a multivariate linear regression model (valid and usable for different individuals), the aim of this paper is the assessment of some techniques usable for determining such a model, namely Ordinary Least Squares (OLS), Partial Least Squares (PLS) and Least Absolute Shrinkage and Selection Operator (LASSO). Once the model is identified on a training set, the accuracy of prospective glucose profiles estimated from "unseen" multisensor data is assessed. Preliminary results obtained from 18 in-clinic study days show that sufficiently accurate reconstruction of glucose levels can be achieved if suitable model identification techniques, such as LASSO, are considered