Remote Inference of Cognitive Scores in ALS Patients Using a Picture
  Description
Carla Agurto Guillermo Cecchi Bo Wen Ernest Fraenkel James Berry Indu Navar Raquel Norel
Amyotrophic lateral sclerosis is a fatal disease that not only affects
movement, speech, and breath but also cognition. Recent studies have focused on
the use of language analysis techniques to detect ALS and infer scales for
monitoring functional progression. In this paper, we focused on another
important aspect, cognitive impairment, which affects 35-50% of the ALS
population. In an effort to reach the ALS population, which frequently exhibits
mobility limitations, we implemented the digital version of the Edinburgh
Cognitive and Behavioral ALS Screen (ECAS) test for the first time. This test
which is designed to measure cognitive impairment was remotely performed by 56
participants from the EverythingALS Speech Study. As part of the study,
participants (ALS and non-ALS) were asked to describe weekly one picture from a
pool of many pictures with complex scenes displayed on their computer at home.
We analyze the descriptions performed within +/- 60 days from the day the ECAS
test was administered and extract different types of linguistic and acoustic
features. We input those features into linear regression models to infer 5 ECAS
sub-scores and the total score. Speech samples from the picture description are
reliable enough to predict the ECAS subs-scores, achieving statistically
significant Spearman correlation values between 0.32 and 0.51 for the model's
performance using 10-fold cross-validation.