Estimation of Dementia Tendency by Integrating Sleep Behavioral and Physiological data

<p>In recent years, there has been concern that the number of patients with dementia will increase as the population ages. On the other hand, if the tendency toward dementia can be detected at an early stage, it may be possible to delay the progression of symptoms by providing appropriate treatment. Against this background, the establishment of a behavioral recognition model for estimating dementia tendency based on behavioral information is one of the most important issues in health care technology. In this study, we focus on the findings that dementia tends to be associated with reduced daytime activity, sleep disturbance, and irregular sleep patterns. First, we obtained various behavioral data from 132 subjects, including those diagnosed with dementia, and created a data set that included the Mini Mental State Examination (MMSE) at the time of measurement. The behavioral data obtained were specifically activity per minute, heart rate, and respiration rate, each of which was obtained for 24 hours for each subject. In this presentation, we report the results of the construction and evaluation of a classification model for MMSE scores indicating dementia tendency using the dataset. The result of the three-class classification using Random Forest was 0.459 in F-value. We also report the results of visualization of the contribution using SHAP in this case.</p>