# Eye-Movement behavior identification for AD diagnosis

arXiv: https://arxiv.org/abs/1702.00837 · HTML: https://mlsanity.com/paper/1702.00837/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2017.

## Built on by (most cited first, 2 of 2)

- [Deep Learning-based Eye-Tracking Analysis for Diagnosis of Alzheimer's Disease Using 3D Comprehensive Visual Stimuli](https://mlsanity.com/paper/2303.06868/) (2023)
- [Classification of Alzheimers Disease with Deep Learning on Eye-tracking Data](https://mlsanity.com/paper/2309.12574/) (2023)

## Abstract (arXiv)

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.