Clinical classification of memory and cognitive impairment with multimodal digital biomarkers

Abstract

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