Abstract
Autism spectrum disorder (ASD) is an early developmental disorder characterized by mutation of enculturation associated with attention deficit disorder in the visual perception of emotional expressions. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. Data analysis and classification of ASD is still challenging due to unsolved issues arising from many severity levels and range of signs and symptoms. To understanding the functions which involved in autism, neuroscience technology analyzed responses to stimuli of autistic audio and video. The study focuses on analyzing the data set of adults and children with ASD using practical component analysis method. To satisfy this aim, the proposed method consists of three main stages including: (1) data set preparation, (2) Data analysis, and (3) Unsupervised Classification. The experimental results were performed to classify adults and children with ASD. The classification results in adults give a sensitivity of 78.6% and specificity of 82.47%, while the classification results in children give a sensitivity of 87.5% and specificity of 95.7%.
Highlights
Autism spectrum disorder (ASD) is a condition that can be characterized by a constant deficit in social communication, social interaction, and the presence of restrictive and repetitive behavior
It is an early developmental disorder characterized by alterations in socialization associated with a deficit in the visual perception of faces and emotional expressions. is deficit in the perception of faces and emotional expressions seems to be linked to the peculiarities of the gaze in autistic pathology [1]. e study of this behavioral disorder is carried out by the measurement of different ocular parameters during the perception of neutral and emotional faces [2]
It is generally recognized that traditional clinical methods have difficulty in well distinguishing patients from healthy controls (HC) [5]. erefore, data analysis and classification of ASD is still challenging due to unsolved issues arising from many severity levels and range of signs and symptoms. e commonly used tools for analyzing the dataset of autism are functional magnetic resonance imaging, Electroencephalography (EEG), and more recently “eye tracking”
Summary
Autism spectrum disorder (ASD) is a condition that can be characterized by a constant deficit in social communication, social interaction, and the presence of restrictive and repetitive behavior It is an early developmental disorder characterized by alterations in socialization associated with a deficit in the visual perception of faces and emotional expressions. E study of this behavioral disorder is carried out by the measurement of different ocular parameters (fixation time, distance and speed of exploration, ocular path) during the perception of neutral (with direct or deviant gaze) and emotional faces (expressing joy or sadness) [2]. Eye tracking is a system of monitoring of the gaze grouping together a set of techniques which make it possible to record the ocular movements and to measure several parameters such as the time of fixation of the image, the number of fixations of an area of the image, etc. Two ways will be followed, first concerns the development of classifiers based on Classi cation Results in Adults & in Children
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