Detecting cognitive impairment in cerebrovascular disease using gait, dual tasks, and machine learning.

Abstract

Background: Cognitive impairment is common after a stroke, but it can often go undetected. In this study, we investigated whether using gait and dual tasks could help detect cognitive impairment after stroke.

Methods: We analyzed gait and neuropsychological data from 47 participants who were part of the Ontario Neurodegenerative Disease Research Initiative. Based on neuropsychological criteria, participants were categorized as impaired (n = 29) or cognitively normal (n = 18). Nested cross-validation was used for model training, hyperparameter tuning, and evaluation. Grid search with cross-validation was used to optimize the hyperparameters of a set of feature selectors and classifiers. Different gait tests were assessed separately.

Results: The best classification performance was achieved using a comprehensive set of gait metrics, measured by the electronic walkway, that included dual-task costs while performing subtractions by ones. Using a Support Vector Machine (SVM), we could achieve a sensitivity of 96.6%, and a specificity of 61.1%. An optimized threshold of 27 in the Montreal Cognitive Assessment (MoCA) revealed lower classification performance than the gait metrics, although differences in classification results were not significant. Combining the classifications provided by MoCA with those provided by gait metrics in a majority voting approach resulted in a higher specificity of 72.2%, and a high sensitivity of 93.1%.

Conclusions: Our results suggest that gait analysis can be a useful tool for detecting cognitive impairment in patients with cerebrovascular disease, serving as a suitable alternative or complement to MoCA in the screening for cognitive impairment.