Prediction of dyslexia severity levels from fixation and saccadic eye movement using machine learning

Abstract

BackgroundDyslexia is a neurological disorder that causes poor reading and comprehension skills. Dyslexics experience problems in understanding phonemes of languages hence they show less ability to relate letters to form words and sentences. Dyslexia is not directly related to vision but it has been observed that most dyslexics have a magnocellular deficit which causes abnormal eye movements. From the literature, it has been observed that atypical eye movement could be a strong indicator of dyslexia. MethodsThis research work identifies the severity levels of dyslexia from fixations and saccade eye movement events based on eye gaze points. Fixations and saccades are computed using a velocity threshold algorithm. Features related to fixations and saccades are used to divide dyslexics into high and low based on their severity levels. Unsupervised learning approach K-Means clustering is adopted to group the dyslexics into high and low groups. Cluster evaluation measures such as Silhouette analysis, Elbow Method, and Davies Bouldin Index are used to find the optimal value of K. The quality of the clusters is further reinforced by Multivariate analysis of variance (MANOVA) analysis. Data in the clusters are visualized to understand the data distribution and range of different features. ResultsK-means clustering gave two clusters – high and low dyslexics. The Silhouette score was high and the Davies Bouldin Index was low showing that the clusters are of good quality. MANOVA analysis has given a p-value less than 0.01 which further reinforces the quality of clusters to be good. The data in clusters are visualized, analyzed, and compared to existing eye movement findings on dyslexia in literature. ConclusionIt has been observed that features such as fixation duration, number of fixations, saccade duration, and number of saccades act as key indicators to identify the severity levels of dyslexia. The characteristics of data in the clusters generated using these features match with the characteristics of existing eye movement findings, hence the data value ranges can be used to identify the severity levels of dyslexia.