Using wireless dry-sensor EEG wearables and an AI-based neural algorithms to detect early-stage AD/ADRD
Awardee Organization(s): Cogwear LLC
Principal Investigator(s): David Yonce, MS, MBA
Official Project Title: Physiological Detection and Monitoring of Alzheimer’s Disease
AITC Partner: PennAITech
Website(s): www.cogweartech.com
Cogwear has developed a wireless dry-sensor EEG wearable that can easily collect clinical-grade brainwave data anywhere, anytime with comfort and no limits on mobility. Initially applied to behavioral health, here we propose to extend our platform to early detection and trending of dementia and Alzheimer’s Disease (AD) based upon brain physiology. With the ability to sense EEG from the frontal and temporal lobes, the parts of the brain that regulate short and long-term memory, planning, and executive functions, our system can detect EEG changes implicated in dementia and AD: slowing, reduced complexity, decrease in synchronization, and neuromodulatory deficits.
Our project will focus on two components: migrating our wearable to a soft goods form factor with downsized electronics more appropriate for in-clinic and home use and begin to develop the EEG signal processing and applications to quantitatively detect and trend brain processes associated with dementia and AD. Deliverables will include an advanced prototype and pilot testing of algorithms with humans in a small sample of healthy and AD/ADRD patients.
Our expectation is that these algorithms will ultimately show efficacy to detect presymptomatic brain changes, allowing intervention by caregivers to prepare patients and families. Further, because subtle EEG shifts can be indicative of changing disease states, we can provide quantitative trending of AD based upon brain physiology, providing new methods to titrate pharmaceuticals and evaluate disease treatments. Through earlier detection and enhanced monitoring, our goal is to better support patients and their families by enabling more years of high-functioning and independent living.