Predictive Model for Dyslexia from Fixations and Saccadic Eye Movement Events. (October 2020)

• Record Type: Journal Article
• Authors: Jothi Prabha, A; Bhargavi, R
• Is Part Of: Computer methods and programs in biomedicine. Volume 195(2020)
• Publication Date: 2020-10
• Subjects: Dispersion-threshold identification algorithms -- Hybrid SVM–PSO -- K-Nearest Neighbor -- Logistic Regression -- Random Forest Classifier -- Support Vector Machine -- Statistical features -- Velocity-threshold identification algorithm; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105538
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Explore and identify a set of eye movement features that contribute more for prediction of dyslexia using machine automatically A machine learning model to classify dyslexics and non-dyslexics using the identified eye movement features Abstract: Background: Dyslexia is a disorder characterized by difficulty in reading such as poor speech and sound recognition. They have less capability to relate letters and form words and exhibit poor reading comprehension. Eye-tracking methodologies play a major role in analyzing human cognitive processing. Dyslexia is not a visual impairment disorder but it's a difficulty in phonological processing and word decoding. These difficulties are reflected in their eye movement patterns during reading. Objective: The disruptive eye movement helps us to use eye-tracking methodologies for identifying dyslexics. Methods: In this paper, a small set of eye movement features have been proposed that contribute more to distinguish between dyslexics and non-dyslexics by machine learning models. Features related to eye movement events such as fixations and saccades are detected using statistical measures, dispersion threshold identification (I-DT) and velocity threshold identification (I-VT) algorithms. These features were further analyzed using various machine learning algorithms such as Particle Swarm Optimization (PSO) based SVM Hybrid Kernel (Hybrid SVM – PSO), Support Vector Machine (SVM), Random Forest classifier (RF), Logistic Regression (LR) and K-Nearest Neighbor (KNN) for classification of dyslexics and non-dyslexics. Results: The accuracy achieved using the Hybrid SVM –PSO model is 95.6 %. The best set of features that gave high accuracy are average no of fixations, average fixation gaze duration, average saccadic movement duration, total number of saccadic movements, and average number of fixations. Conclusion: It is observed that eye movement features detected using velocity-based algorithms performed better than those detected by dispersion-based algorithms and statistical measures.