2,231 research outputs found

    Development of holistic classification systems for children with cerebral palsy

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    Cerebral palsy (CP) is a complex disorder. There is a gap in the literature in classifying children with CP broadly. The purpose of this thesis was to develop holistic classification systems for children with CP. As a first step, a search was conducted to explore the strategies used to classify children with developmental co-ordination disorder and autism-spectrum disorder. Two versions of holistic classification systems named the body function index in cerebral palsy (BFI-CP) versions I and II were developed using two methods. Then, the relationship and differences among the developed classification systems and the Gross Motor Function Classification System (GMFCS) were explored. Next, differences among subsets of the classifications that did not correspond to the ordinal levels of the GMFCS were explored. Next, the relationships between the developed classification systems (BFICP- I and II) and the GMFCS and the change in outcome of motor function were explored. Exploration of the existing classification systems of childhood disorders (Chapter 2) demonstrated that none of the classification systems in CP addressed the majority of the key features in the international consensus definition of CP. The BFI-CP I was developed using a summing technique and the BFI-CP II was developed using cluster analysis. The findings demonstrated a strong correlation between the BFI-CP I and the GMFCS (r=0.92), the BFI-CP II and the GMFCS (r=0.93), and the BFI-CP I and II (r=0.92), all (pχ² = 670.49, df=16, pχ² =685.57, df=16,

    Atypical eye contact in autism: Models, mechanisms and development

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    An atypical pattern of eye contact behaviour is one of the most significant symptoms of Autism Spectrum Disorder (ASD). Recent empirical advances have revealed the developmental, cognitive and neural basis of atypical eye contact behaviour in ASD. We review different models and advance a new ‘fast-track modulator model’. Specifically, we propose that atypical eye contact processing in ASD originates in the lack of influence from a subcortical face and eye contact detection route, which is hypothesized to modulate eye contact processing and guide its emergent specialization during development

    An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

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    Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings

    An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

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    Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings

    A Survey of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data

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    Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders among children, that affects different areas in the brain that allows executing certain functionalities. This may lead to a variety of impairments such as difficulties in paying attention or focusing, controlling impulsive behaviours and overreacting. The continuous symptoms may have a severe impact in the long-term. This paper explores the ADHD identification studies using eye movement data and functional Magnetic Resonance Imaging (fMRI). This study discusses different machine learning techniques, existing models and analyses the existing literature. We have identified the current challenges and possible future directions to provide computational support for early identification of ADHD patients that enable early treatments

    Functional network and spectral analysis of clinical EEG data to identify quantitative biomarkers and classify brain disorders

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    Many cognitive and neurological disorders today, such as Autism Spectrum Disorders (ASD) and various forms of epilepsy such as infantile spasms (IS), manifest as changes in voltage activity recorded in scalp electroencephalograms (EEG). Diagnosis of brain disease often relies on the interpretation of complex EEG features through visual inspection by clinicians. Although clinically useful, such interpretation is subjective and suffers from poor inter-rater reliability, which affects clinical care through increased variability and uncertainty in diagnosis. In addition, such qualitative assessments are often binary, and do not parametrically measure characteristics of disease manifestations. Many cognitive disorders are grouped by similar behaviors, but may arise from distinct biological causes, possibly represented by subtle electrophysiological differences. To address this, quantitative analytical tools - such as functional network connectivity, frequency-domain, and time-domain features - are being developed and applied to clinically obtained EEG data to identify electrophysiological biomarkers. These biomarkers enhance a clinician’s ability to accurately diagnose, categorize, and select treatment for various neurological conditions. In the first study, we use spectral and functional network analysis of clinical EEG data recorded from a population of children to propose a cortical biomarker for autism. We first analyze a training set of age-matched (4–8 years) ASD and neurotypical children to develop hypotheses based on power spectral features and measures of functional network connectivity. From the training set of subjects, we derive the following hypotheses: 1) The ratio of the power of the posterior alpha rhythm (8–14 Hz) peak to the anterior alpha rhythm peak is significantly lower in ASD than control subjects. 2) The functional network density is lower in ASD subjects than control subjects. 3) A select group of edges provide a more sensitive and specific biomarker of ASD. We then test these hypotheses in a validation set of subjects and show that both the first and third hypotheses, but not the second, are validated. The validated features successfully classified the data with significant accuracy. These results provide a validated study for EEG biomarkers of ASD based on changes in brain rhythms and functional network characteristics. We next perform a follow-up study that utilizes the same group of ASD and neurotypical subjects, but focuses on differences between these two groups in the sleep state. Motivated by the results from the previous study, we utilize the previously validated biomarkers, including the alpha ratio and the subset of edges found to be a sensitive biomarker of ASD, and test their effectiveness in the sleep state. To complement these frequency domain features, we also investigate the efficacy of several time domain measures. This investigation did not lead to significant findings, which may have important implications for the differences between sleep and wake states in ASD, or perhaps generally for clinical assessment, as well as for the effect of noise on signal in clinically obtained data. Finally, we design a similar analysis framework to investigate a set of clinical EEG data recorded from a population of children with active infantile spasms (IS) (2-16 months), and age-matched neurotypical children, in both wake and sleep states. The goal of this analysis is to develop a quantitative biomarker from the EEG signal, which ultimately we will apply to predict the clinical outcome of children with IS. In addition to spectral and functional network analysis, we calculate time domain features previously found to correlate with seizures. We compare the two populations by each feature individually, test the effects of age on these features, use all features in a linear discriminant model to categorize IS versus neurotypical EEG, and test the findings using a leave-one-out validation test. We find almost every feature tested shows significant population differences between IS and control groups, and that taken together they serve as an effective classifier, with potential to be informative as to disease severity and long-term outcome. Furthermore, analysis of these features reveals two groups, indicating a possibility that these features reflect two distinct qualitative characteristics of IS and seizures

    Machine Learning and Virtual Reality on Body Movements¿ Behaviors to Classify Children with Autism Spectrum Disorder

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    [EN] Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements' frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients' subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements' biomarkers that could contribute to improving ASD diagnosis.This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness funded project "Immersive virtual environment for the evaluation and training of children with autism spectrum disorder: T Room" (IDI-20170912) and by the Generalitat Valenciana funded project REBRAND (PROMETEO/2019/105). Furthermore, this work was co-founded by the European Union through the Operational Program of the European Regional development Fund (ERDF) of the Valencian Community 2014-2020 (IDIFEDER/2018/029).Alcañiz Raya, ML.; Marín-Morales, J.; Minissi, ME.; Teruel Garcia, G.; Abad, L.; Chicchi-Giglioli, IA. (2020). 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