Detecting Autism Spectrum Disorder with Deep Eye Movement Features

2026-01-09

arXiv.org

AI Summary

This study addresses the challenge of capturing subtle behavioral markers in traditional diagnostic approaches for Autism Spectrum Disorder (ASD) by proposing a Discrete Short-Term Sequence modeling framework (DSTS) tailored for eye-tracking data. DSTS eschews global attention mechanisms in favor of modeling local temporal dependencies, integrating class-aware representation learning with an imbalance-aware mechanism to effectively accommodate the discrete and locally structured nature of eye-movement signals. Extensive experiments on multiple public eye-tracking datasets demonstrate that DSTS significantly outperforms existing machine learning and deep learning methods, achieving superior accuracy, sensitivity, and robustness in automated ASD detection tasks.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Time-Series/Data Streams
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and behavioral patterns. Eye movement data offers a non-invasive diagnostic tool for ASD detection, as it is inherently discrete and exhibits short-term temporal dependencies, reflecting localized gaze focus between fixation points. These characteristics enable the data to provide deeper insights into subtle behavioral markers, distinguishing ASD-related patterns from typical development. Eye movement signals mainly contain short-term and localized dependencies. However, despite the widespread application of stacked attention layers in Transformer-based models for capturing long-range dependencies, our experimental results indicate that this approach yields only limited benefits when applied to eye movement data. This may be because discrete fixation points and short-term dependencies in gaze focus reduce the utility of global attention mechanisms, making them less efficient than architectures focusing on local temporal patterns. To efficiently capture subtle and complex eye movement patterns, distinguishing ASD from typically developing (TD) individuals, a discrete short-term sequential (DSTS) modeling framework is designed with Class-aware Representation and Imbalance-aware Mechanisms. Through extensive experiments on several eye movement datasets, DSTS outperforms both traditional machine learning techniques and more sophisticated deep learning models.

Problem

• Autism Spectrum Disorder
• Eye Movement
• Short-term Dependencies
• Behavioral Markers
• Diagnostic Detection

Innovation

• Discrete Short-Term Sequential
• Class-aware Representation
• Imbalance-aware Mechanism
• Eye Movement Analysis
• Autism Spectrum Disorder Detection