Total: 1
Parkinson's disease (PD) is characterized by motor dysfunction; however, non-motor symptoms such as cognitive decline also have a dramatic impact on quality of life. Current assessments to diagnose cognitive impairment take many hours and require high clinician involvement. Thus, there is a need to develop new tools leading to quick and accurate determination of cognitive impairment to allow for appropriate, timely interventions. In this paper, individuals with PD, designated as either having no cognitive impairment (NCI) or mild cognitive impairment (MCI), undergo a speech-based protocol, involving reading or listing items within a category, performed either with or without a concurrent drawing task. From the speech recordings, we extract motor coordination-based features, derived from correlations across acoustic features representative of speech production subsystems. The correlation-based features are utilized in gaussian mixture models to discriminate between individuals designated NCI or MCI in both the single and dual task paradigms. Features derived from the laryngeal and respiratory subsystems, in particular, discriminate between these two groups with AUCs > 0.80. These results suggest that cognitive impairment can be detected using speech from both single and dual task paradigms, and that cognitive impairment may manifest as differences in vocal fold vibration stability.