Eye-Movement behavior identification for AD diagnosis
This work addresses Alzheimer's disease diagnosis, but it is incremental as it applies existing deep-learning methods to a new dataset of eye-tracking data.
The researchers tackled the problem of diagnosing Alzheimer's disease by differentiating eye-movement behavior during reading between patients and healthy controls using a deep-learning approach, achieving promising results that may help understand the dynamics and neuropsychological correlates.
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.