Auxiliary Diagnostic Method for Early Autism Spectrum Disorder Based on Eye Movement Data Analysis

Autism spectrum disorder (ASD) is a comprehensive mental development disorder characterized by abnormal interpersonal communication and interaction patterns, narrow scope of interests, and limited content of activities. Due to the lack of biological diagnostic indicators, the current diagnosis of ASD mainly relies on experts’ comprehensive clinical analysis of children, which is usually subjective and highly dependent on doctors’ individual professional skills. In this study, we propose an auxiliary diagnostic method for early ASD, which is based on the eye movement data analysis of autistic children. The method involves biological motion visualization, eye tracking, machine learning, and other related techniques. More specifically, the visualized biological motion animation is divided into five stages according to different biological behaviors of human skeletal figures presented in the animation. At the same time, the screen is divided into six areas to represent different regions the children gaze at. Following these two temporal and spatial principles, features can be extracted from eye movement data. Based on those extracted features, machine learning methods, particularly KNN, Gaussian-NB, and Cubic-SVM, are trained to classify and diagnose autistic children, making future timely treatment possible.

1 Introduction

The mental retardation of ASD, which increases with age, is mainly manifested through several clinical features, including adaptive ability, language, and social abilities [1]. Therefore, the early and accurate diagnosis of ASD is particularly important in order to reduce the intellectual deficiency of autistic children and improve their prognosis. The existing diagnostic method for ASD mainly depends on doctors’ level of experience, which makes the diagnostic results quite subjective and variable. There are many scoring scales for ASD diagnosis worldwide, such as the Achenbach System of Empirically Based Assessment (ASEBA) scales [2]. Each of the scales requires doctors to diagnose ASD through observation and communication. In order to eliminate these subjective factors and diagnose children without linguistic ability in an early stage, biological and scientific diagnostic indicators should be systematically employed to diagnose ASD. Therefore, in this paper, machine learning is investigated for effective diagnosis of ASD children based on eye movement data analysis.