Investigators:
Joyita Dutta, UMass Amherst
MassAITC Cohort: Year 2 (AD/ADRD)
Initial Proposal Abstract: This academic-industrial partnership between UMass Amherst and CGX Systems will develop AI techniques for sleep staging in seniors (>65 years) using multimodal data from two wearable devices. We will validate a wearable EEG headband and a smartwatch for sleep staging and characterization in the elderly. Sleep disturbances are among the earliest observable symptoms of Alzheimer’s disease (AD). While many wearables are available for sleep monitoring, most studies are based on young subjects. Our work will impact early identification of seniors at risk of AD, opening up a large elderly market for wearable devices like EEG headbands and smartwatches.
Outcomes:
- Publication: AI-Driven Sleep Staging Using Instantaneous Heart Rate and Accelerometry: Insights from an Apple Watch Study
Authors: Tzu-An Song, Yubo Zhang, Ziyuan Zhou, Luke Hou, Masoud Malekzadeh, Aida Behzad, Joyita Dutta Abstract Polysomnography, the gold standard for sleep evaluations, involves complex setup and data acquisition protocols and requires manual scoring of sleep data. Smartwatches and other multi-sensor consumer wearable devices with automated sleep staging capabilities offer… Read more: Publication: AI-Driven Sleep Staging Using Instantaneous Heart Rate and Accelerometry: Insights from an Apple Watch Study - New Product Launch: BIDSleep iPhone and Apple Watch App
BIDSleep helps you access wellness data from your Apple Watch, including heart rate, motion, blood oxygen, and sleep stages, all securely stored on your device. App Purpose: BIDSleep is a wellness app that helps users collect heart rate, blood oxygen (SpO₂), motion, and optional sleep stage data during rest or… Read more: New Product Launch: BIDSleep iPhone and Apple Watch App - Open Source AI-model Released: SLAMSS-IFS
To the study team’s knowledge, this is the first open-source four-class sleep staging model developed from a multi-night Apple Watch sleepstudy. SLAMSS-IFS, an advanced version of our previous SLAMSS model, for four-class sleep staging using IHR and accelerometry signals fromthese wearable devices. Key innovations in the model, including an intra-epoch… Read more: Open Source AI-model Released: SLAMSS-IFS - Grant Funding: R01 AG082354
Title: Genomics-guided sleep biomarker discovery for early Alzheimer’s disease: A wearables study This R01 builds upon the technology and algorithms for sleep-based metrics developed in the MassAITC pilot project, utilizing the same EEG device. It shifts the study from a general AD-risk factor population to a genetic AD-risk factor population.… Read more: Grant Funding: R01 AG082354