Detecting Autism Spectrum Disorder with Deep Eye Movement Features

TL;DR

This paper introduces a novel discrete short-term sequential framework with class-aware and imbalance-aware mechanisms for detecting Autism Spectrum Disorder using eye movement data, emphasizing local temporal patterns over global attention.

Contribution

The paper proposes a new DSTS framework tailored for eye movement data, demonstrating superior performance over existing methods in ASD detection.

Findings

DSTS outperforms traditional machine learning models.

DSTS surpasses other deep learning approaches.

Local temporal patterns are more effective than global attention for this task.

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.