Discriminative Sleep Patterns of Alzheimer's Disease via Tensor Factorization
This research addresses the potential role of sleep as a risk factor for Alzheimer's disease, though it is preliminary and incremental in nature.
The study tackled the problem of identifying sleep patterns linked to Alzheimer's disease by applying supervised tensor factorization to EEG data from 83 cases and 331 controls, extracting 30 patterns and identifying 5 significant ones (4 for AD, 1 for normal) that relate to conditions like wakefulness and insomnia.
Sleep change is commonly reported in Alzheimer's disease (AD) patients and their brain wave studies show decrease in dreaming and non-dreaming stages. Although sleep disturbance is generally considered as a consequence of AD, it might also be a risk factor of AD as new biological evidence shows. Leveraging National Sleep Research Resource (NSRR), we built a unique cohort of 83 cases and 331 controls with clinical variables and EEG signals. Supervised tensor factorization method was applied for this temporal dataset to extract discriminative sleep patterns. Among the 30 patterns extracted, we identified 5 significant patterns (4 patterns for AD likely and 1 pattern for normal ones) and their visual patterns provide interesting linkage to sleep with repeated wakefulness, insomnia, epileptic seizure, and etc. This study is preliminary but findings are interesting, which is a first step to provide quantifiable evidences to measure sleep as a risk factor of AD.