Eye-Movement behavior identification for AD diagnosis

TL;DR

This paper presents a deep learning approach to distinguish eye-movement patterns of individuals with neurodegenerative diseases, specifically Alzheimer's, from healthy controls during reading tasks, aiding in diagnosis.

Contribution

It introduces a novel deep learning framework utilizing denoising autoencoders and a neural network to analyze eye-tracking data for Alzheimer's detection.

Findings

Models show promising accuracy in differentiating AD from controls.

Eye-movement dynamics correlate with neuropsychological factors.

Method could assist early diagnosis of neurodegenerative diseases.

Abstract

In the present work, we develop a deep-learning approach for differentiating the eye-movement behavior of people with neurodegenerative diseases over healthy control subjects during reading well-defined sentences. We define an information compaction of the eye-tracking data of subjects without and with probable Alzheimer's disease when reading a set of well-defined, previously validated, sentences including high-, low-predictable sentences, and proverbs. Using this information we train a set of denoising sparse-autoencoders and build a deep neural network with these and a softmax classifier. Our results are very promising and show that these models may help to understand the dynamics of eye movement behavior and its relationship with underlying neuropsychological correlates.