  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.
