Remote Inference of Cognitive Scores in ALS Patients Using a Picture Description

TL;DR

This study demonstrates that remote analysis of speech during picture description tasks can reliably predict cognitive impairment scores in ALS patients, enabling non-invasive monitoring of cognitive decline.

Contribution

First implementation of a digital, remote ECAS cognitive assessment for ALS patients using speech analysis from picture descriptions.

Findings

Speech features predict ECAS sub-scores with correlations up to 0.51.

Remote speech analysis can reliably infer cognitive impairment in ALS.

Models show statistically significant correlations in cross-validation.

Abstract

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.