Analysis of eye gaze pattern of infants at risk of autism spectrum disorder using Markov models

Abstract

This paper presents the possibility of using pattern recognition algorithms of infant gaze patterns at six months of age among children at high risk for an autism spectrum disorder (ASD). ASDs, which must be diagnosed by 3 years of age, are characterized by communication and interaction impairments which frequently involve disturbances of visual attention and gaze patterning. We used video cameras to record the face-to-face interactions of 32 infant subjects with their parents. The video was manually coded to determine the eye gaze pattern of infants by marking where the infant was looking in each frame (either at their parent's face or away from their parent's face). In order to identify infants ASD diagnosis at three years, we analyzed infant eye gaze patterns at six months. Variable-order Markov Models (VMM) were used to create models for typically developing comparison children as well as children with an ASD. The models correctly classified infants who did and did not develop an ASD diagnosis with an accuracy rate of 93.75 percent. Employing an assessment tool at a very young age offers the hope of early intervention, potentially mitigating the effects of the disorder throughout the rest of the child's life.