Language
English
Publication Date
1-1-2024
Journal
Alzheimer's & Dementia
DOI
10.1002/dad2.12557
PMID
38406610
PMCID
PMC10884988
PubMedCentral® Posted Date
2-23-2024
PubMedCentral® Full Text Version
Post-print
Abstract
Introduction: Early detection of Alzheimer's disease and cognitive impairment is critical to improving the healthcare trajectories of aging adults, enabling early intervention and potential prevention of decline.
Methods: To evaluate multi-modal feature sets for assessing memory and cognitive impairment, feature selection and subsequent logistic regressions were used to identify the most salient features in classifying Rey Auditory Verbal Learning Test-determined memory impairment.
Results: Multimodal models incorporating graphomotor, memory, and speech and voice features provided the stronger classification performance (area under the curve = 0.83; sensitivity = 0.81, specificity = 0.80). Multimodal models were superior to all other single modality and demographics models.
Discussion: The current research contributes to the prevailing multimodal profile of those with cognitive impairment, suggesting that it is associated with slower speech with a particular effect on the duration, frequency, and percentage of pauses compared to normal healthy speech.
Keywords
amnestic MCI, automatic speech recognition, digital clock drawing, mild cognitive impairment, non‐amnestic MCI, speech, verbal memory, voice
Published Open-Access
yes
Recommended Citation
Banks, Russell; Higgins, Connor; Greene, Barry R; et al., "Clinical Classification of Memory and Cognitive Impairment With Multimodal Digital Biomarkers" (2024). Faculty, Staff and Students Publications. 8451.
https://digitalcommons.library.tmc.edu/baylor_docs/8451