IMR Press / JIN / Volume 17 / Issue 4 / DOI: 10.31083/j.jin.2018.04.0416
Open Access Research article
Eye movement behavior identification for Alzheimer’s disease diagnosis
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1 Laboratorio de Desarrollo en Neurociencia Cognitiva, Instituto de Investigaciones en Ingeniería Eléctrica (IIIE), Departamento de Ingeniería Eléctrica y de Computadoras (DIEC), Universidad Nacional del Sur (UNS) - CONICET, San Andrés 800 – Bahía Blanca, 8000, Buenos Aires, Argentina
2 Laboratorio de Visualización y Computación Gráfica (VyGLab), Departamento de Ciencias e Ingeniería de la Computación (DCIC), Universidad Nacional del Sur (UNS), San Andrés 800 – Bahía Blanca, 8000, Buenos Aires, Argentina
3 Comisión de Investigaciones Científicas de la Provincia de Buenos Aires (CIC), San Andrés 800 – Bahía Blanca, 8000, Buenos Aires, Argentina
*Correspondence: juan.biondi@uns.edu.ar (Juan Biondi)
J. Integr. Neurosci. 2018, 17(4), 349–354; https://doi.org/10.31083/j.jin.2018.04.0416
Submitted: 19 June 2017 | Accepted: 9 January 2018 | Published: 15 November 2018
Copyright: © 2018 The authors. Published by IMR press.
This is an open access article under the CC BY–NC 4.0 license.https://creativecommons.org/licenses/by-nc/4.0/
Abstract

We develop a deep-learning approach to differentiate between the eye movement behavior of people with neurodegenerative diseases during reading compared to healthy control subjects. The subjects with and without Alzheimer’s disease read well-defined and previously validated sentences including high- and low-predictable sentences, and proverbs. From these eye-tracking data trial-wise information is derived consisting of descriptors that capture the reading behavior of the subjects. With this information a set of denoising sparse-autoencoders are trained and a deep neural network is built using the trained autoencoders and a softmax classifier that identifies subjects with Alzheimer’s disease with 89.78% accuracy. The results are very encouraging and show that such models promise to be helpful for understanding the dynamics of eye movement behavior and its relation with underlying neuropsychological processes.

Keywords
Eye-tracking
Deep-learning
Alzheimer’s disease
neurodegenerative diseases
eye movement behavior
neuropsychological processes
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