2017/02/02 by Biondi, Juan, Fernandez, Gerardo, Castro, Silvia +1
#FOS: Biological sciences #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Neurons and Cognition (q-bio.NC)
paper · doi:10.48550/arxiv.1702.00837
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.