PRESENT AND FUTURE PERVASIVE HEALTHCARE METHODOLOGIES: INTELLIGENT BODY DEVICES, PROCESSING AND MODELING TO SEARCH FOR NEW CARDIOVASCULAR AND PHYSIOLOGICAL BIOMARKERS

The motivation behind this work comes from the area of pervasive computing technologies for healthcare and wearable healthcare IT systems, an emerging field of research that brings in revolutionary paradigms for computing models in the 21st century. The aim of this thesis is focused on emerging personal health technologies and pattern recognition strategies for early diagnosis and personalized treatment and rehabilitation for individuals with cardiovascular and neurophysiological diseases. Attention was paid to the development of an intelligent system for the automatic classification of cardiac valve disease for screening purposes. Promising results were reported with the possibility to implement a new screening strategy for the diagnosis of cardiac valve disease in developing countries. A novel assistive architecture for the elderly able to non-invasively assess muscle fatigue by surface electromyography using wireless platform during exercise with an ergonomic platform was presented. Finally a wearable chest belt for ECG monitoring to investigate the psycho-physiological effects of the autonomic system and a wearable technology for monitoring of knee kinematics and recognition of ambulatory activities were characterized to evaluate the reliability for clinical purposes of collected data. The potential impact in the clinical arena of this research would be extremely important, since promising data show how such emerging personal technologies and methodologies are effective in several scenarios to early screening and discovery of novel diagnostic and prognostic biomarkers

A personal decision support system for heart failure management (HeartMan) : study protocol of the HeartMan randomized controlled trial

Background: Heart failure (HF) is a highly prevalent chronic disease, for which there is no cure available. Therefore, improving disease management is crucial, with mobile health (mHealth) being a promising technology. The aim of the HeartMan study is to evaluate the effect of a personal mHealth system on top of standard care on disease management and health-related quality of life (HRQoL) in HF. Methods: HeartMan is a randomized controlled 1:2 (control: intervention) proof-of-concept trial, which will enrol 120 stable ambulatory HF patients with reduced ejection fraction across two European countries. Participants in the intervention group are equipped with a multi-monitoring health platform with the HeartMan wristband sensor as the main component. HeartMan provides guidance through a decision support system on four domains of disease management (exercise, nutrition, medication adherence and mental support), adapted to the patient's medical and psychological profile. The primary endpoint of the study is improvement in self-care and HRQoL after a six-months intervention. Secondary endpoints are the effects of HeartMan on: behavioural outcomes, illness perception, clinical outcomes and mental state. Discussion: HeartMan is technologically the most innovative HF self-management support system to date. This trial will provide evidence whether modern mHealth technology, when used to its full extent, can improve HRQoL in HF

Validation of the Quantitative Checklist for Autism in Toddlers in an Italian Clinical Sample of Young Children With Autism and Other Developmental Disorders.

Background: The Quantitative Checklist for Autism in Toddlers (Q-CHAT) is parent-report screening questionnaire for detecting threshold and sub-threshold autistic features in toddlers. The Q-CHAT is a dimensional measure normally distributed in the general population sample and is able to differentiate between a group of children with a diagnosis of autism and unselected toddlers. Objectives: We aim to investigate the psychometric properties, score distribution, and external validity of the Q-CHAT in an Italian clinical sample of young children with autism versus children with developmental delay and typically developing children. Method: N = 126 typically developing children (TD), n = 139 children with autism, and n = 50 children presenting developmental delay (DD) were administered the Q-CHAT. Standardized measures of cognitive functions, language, and behaviors were also obtained. Results: The Q-CHAT scores were normally distributed and demonstrated adequate internal consistency and good item to total score correlations. The mean Q-CHAT score in the autism group was significantly higher than those found in the DD sample and TD children. No difference on the mean Q-CHAT score between DD and TD children was found. The accuracy of the Q-CHAT to discriminate between autism and TD was very good. Two different cut-points (27 and 31, respectively) maximized sensitivity and specificity for autism versus TD and DD, respectively. Finally, higher Q-CHAT scores were correlated with lower language and social communication skills. Conclusions: In clinical settings, the Q-CHAT demonstrated good psychometric properties and external validity to discriminate autism children not just from children with typical development but also from children with developmental delay

Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening.

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM-recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings

Artificial Intelligence for Dysarthria Assessment in Children With Ataxia: A Hierarchical Approach

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Proof-of-concept trial results of the HeartMan mobile personal health system for self-management in congestive heart failure

This study tested the effectiveness of HeartMan—a mobile personal health system offering decisional support for management of congestive heart failure (CHF)—on health-related quality of life (HRQoL), self-management, exercise capacity, illness perception, mental and sexual health. A randomized controlled proof-of-concept trial (1:2 ratio of control:intervention) was set up with ambulatory CHF patients in stable condition in Belgium and Italy. Data were collected by means of a 6-min walking test and a number of standardized questionnaire instruments. A total of 56 (34 intervention and 22 control group) participants completed the study (77% male; mean age 63 years, sd 10.5). All depression and anxiety dimensions decreased in the intervention group (p < 0.001), while the need for sexual counselling decreased in the control group (p < 0.05). Although the group differences were not significant, self-care increased (p < 0.05), and sexual problems decreased (p < 0.05) in the intervention group only. No significant intervention effects were observed for HRQoL, self-care confidence, illness perception and exercise capacity. Overall, results of this proof-of-concept trial suggest that the HeartMan personal health system significantly improved mental and sexual health and self-care behaviour in CHF patients. These observations were in contrast to the lack of intervention effects on HRQoL, illness perception and exercise capacity

Mindfulness-Based Interventions for Physical and Psychological Wellbeing in Cardiovascular Diseases: A Systematic Review and Meta-Analysis

Background: Recently, there has been an increased interest in the efficacy of mindfulness-based interventions (MBI) for people with cardiovascular diseases (CVD), although the exact beneficial effects remain unclear. Methods: This review aims to establish the role of MBI in the management of wellbeing for patients with CVD. Seventeen articles have been included in this systematic synthesis of the literature and eleven in the meta-analysis. Results: Considering physical (i.e., heart rate, blood pressure) and psychological outcomes (i.e., depression, anxiety, stress, styles of coping), the vast majority of studies confirmed that MBI has a positive influence on coping with psychological risk factors, also improving physiological fitness. Random-effects meta-analysis models suggested a moderate-to-large effect size in reducing anxiety, depression, stress, and systolic blood pressure. Conclusions: Although a high heterogeneity was observed in the methodological approaches, scientific literature confirmed that MBI can now be translated into a first-line intervention tool for improving physical and psychological wellbeing in CVD patients

An Integrated Approach for the Monitoring of Brain and Autonomic Response of Children with Autism Spectrum Disorders during Treatment by Wearable Technologies

Autism Spectrum Disorders (ASD) are associated with physiological abnormalities, which are likely to contribute to the core symptoms of the condition. Wearable technologies can provide data in a semi-naturalistic setting, overcoming the limitations given by the constrained situations in which physiological signals are usually acquired. In this study an integrated system based on wearable technologies for the acquisition and analysis of neurophysiological and autonomic parameters during treatment is proposed and an application on five children with ASD is presented. Signals were acquired during a therapeutic session based on an imitation protocol in ASD children. Data were analyzed with the aim of extracting quantitative EEG (QEEG) features from EEG signals as well as heart rate and heart rate variability (HRV) from ECG. The system allowed evidencing changes in neurophysiological and autonomic response from the state of disengagement to the state of engagement of the children, evidencing a cognitive involvement in the children in the tasks proposed. The high grade of acceptability of the monitoring platform is promising for further development and implementation of the tool. In particular if the results of this feasibility study would be confirmed in a larger sample of subjects, the system proposed could be adopted in more naturalistic paradigms that allow real world stimuli to be incorporated into EEG/psychophysiological studies for the monitoring of the effect of the treatment and for the implementation of more individualized therapeutic programs

Progettazione e realizzazione di un sistema indossabile wireless per l'analisi e classificazione dell'attivita motoria

Negli ultimi decenni l’intelligenza umana sta raggiungendo livelli sempre più elevati in termini di complessità; è riuscita a dar vita a scenari che fino a poco tempo fa dimoravano esclusivamente nell’immaginario collettivo. Ne sono un esempio le tecnologie elettroniche ed informatiche applicate alla medicina dove le conoscenze sono usate al fine di migliorare la qualità della vita. Tutto ciò consente maggiori controlli sulla salute segnalando il ruolo crescente dell’innovazione tecnologica e dell’uso di sistemi avanzati. In ambito biomedico, il settore della Gait Analysis (GA) si pone come obiettivo quello di capire l’intima natura del movimento umano attraverso l’analisi e la quantizzazione del fenomeno stesso resa possibile oggi grazie allo sviluppo delle nanotecnologie, della sensoristica e di tecniche computazionali sempre più avanzate. Tali ricerche si stanno dimostrando preziose nell’arricchire ed integrare i tradizionali metodi di valutazione di funzionalità e, in molti casi, possono fornire informazioni ed indici di grande utilità nella scelta e nel controllo dei percorsi terapeutici ottimi. Lo studio dell’alterazione posturale e motoria in pazienti con problemi motori può dare informazioni molto importanti per conoscere il livello di limitazione funzionale conseguente alla patologia e del suo evolversi nel tempo. Inoltre questi studi forniscono importanti elementi di valutazione dell’efficacia di interventi riabilitativi nel recupero delle alterazioni conseguenti allo stato patologico. Risulta quindi di fondamentale importanza potersi avvalere di tecniche innovative e strumentazioni all’avanguardia che permettano di descrivere, quantificare e valutare il movimento. Un movimento particolarmente significativo della situazione motoria del paziente è la camminata, movimento molto complesso che vede coinvolte sinergicamente diverse articolazioni e risultato di interazioni estremamente raffinate tra i diversi muscoli e le diverse articolazioni. Per questo motivo l’analisi del cammino risulta particolarmente importante avendo come obiettivo l’acquisizione di informazioni cliniche quantitative dettagliate in grado di caratterizzare la deambulazione di un soggetto patologico. In particolare questo obiettivo è raggiunto dalla GA o analisi computerizzata della deambulazione. Mediante questa analisi si ha la possibilità di definire, attraverso l’uso di apparecchiature sofisticate, integrate tra di loro, il pattern spazio-temporale deambulatorio del soggetto in esame. Grazie ad alcune sue importanti proprietà, quali la non invasività, la possibilità di ripetere l’esame più volte in un arco di tempo ridotto, il carattere quantitativo e la tridimensionalità dei dati forniti, essa si pone come fondamentale strumento di indagine nell’analisi del movimento umano. Attualmente i sistemi di GA forniscono informazioni sulla cinematica (velocità e accelerazione degli arti), sulla dinamica (momenti e potenze delle articolazioni) e sull’elettromiografia (attivazione e disattivazione muscolare). Le informazioni sulla cinematica possono essere acquisite attraverso l’uso di sistemi indossabili in grado di rilevare e registrare i segnali del corpo umano. Molto di questo lavoro si concentra sull’utilizzo di sensori tradizionali (ad esempio, elettrocardiografi, impulso ossimetri) per monitorare parametri vitali e per il recupero del movimento di arti danneggiati mediante l’uso di accelerometri e giroscopi. Tra le molteplici applicazioni ritroviamo l'assistenza domiciliare degli anziani e dei disabili, diventata ormai una questione importante per la società futura, mediante una attività di monitoraggio remoto per agevolare la sicurezza di avvertimento e di salva-vita. Le ricerche sui sistemi indossabili inoltre potrebbero essere un metodo alternativo ai costosi sistemi optoelettronici per fornire importanti risultati sulla comprensione della fisiologia del movimento specie nei pazienti disabili con problemi neuro-motori, al fine di effettuare una diagnosi e cura sempre più appropriata. In questo lavoro di tesi è stato realizzato un sistema indossabile versatile e di dimensioni ridotte costituito da un accelerometro realizzato con tecnologia MEMs, collegato ad un microcontrollore ed alimentato a batteria. Ogni dispositivo comunica wireless mediante protocollo Zigbee e trasmette dati Real-Time ad un computer attraverso un’interfaccia grafica opportunamente realizzata. Successivamente i segnali sono stati acquisiti ed elaborati tramite tecniche di pattern recognition e reti neurali artificiali al fine di classificare diversi movimenti. Un tale sistema potrebbe fornire informazioni estremamente importanti per la correzione di disfunzioni neurofisiologiche specie nella postura e nella camminata. Nel primo capitolo è stato introdotto lo scenario di GA in cui si inserisce il seguente lavoro di tesi, riportando lo stato dell’arte sui diversi sistemi di monitoraggio utilizzati attualmente, quali sistemi optoelettronici, sistemi dinamici con piattaforme di forza, sistemi elettromiografici e sistemi portatili. Inoltre è stato descritto l’utilizzo della GA in ambito clinico. Nel secondo capitolo sono state descritte tutte le fasi che hanno portato alla realizzazione e progettazione del dispositivo, analizzando in dettaglio l’accelerometro, il setting del microcontrollore, il protocollo di comunicazione, la realizzazione di un’interfaccia grafica per l’acquisizione del segnale ed il protocollo sperimentale adottato per il posizionamento del dispositivo realizzato. Nel terzo capitolo è stato tracciato un quadro esaustivo sull’analisi dei segnali acquisiti,partendo dall’estrazione dei parametri significativi mediante analisi con wavelet, fino ad arrivare al riconoscimento delle classi di movimento attraverso classificatori a reti neurali. Nel quarto ed ultimo capitolo sono presentati i risultati dei test di valutazione delle prestazioni dei classificatori utilizzati, confrontandoli tra di loro e variando alcuni parametri strutturali delle reti neurali