Cost effective Electroencephalography sensor for monitoring sleep disruption in early stages of Alzheimer's disease

项目总结/文摘

Project Summary/abstract Sleep disruption affects 25–40% of Alzheimer's disease (AD) patients with mild to moderate dementia. Disruption in sleep architecture, distinct from obstructive sleep apnea, is a biomarker highly correlated to the early stages of AD and APOE e4 allele risk factors. Sleep sensors measuring body movement (actigraphy) cannot detect the cyclical patterns that shift between non-rapid eye movement (NREM) and rapid eye movement (REM) sleep stages. Accurate monitoring of sleep architecture requires electroencephalograph (EEG) recordings. Home-based EEG sensors are far from ideal as they are expensive and not comfortable to wear on a daily basis. Large efforts are still needed towards the improvement of electrodes, wireless signal transmission, and overall cost-effectiveness. Being low cost and easy to use are essential factors necessary for public acceptance of large-scale measurements with millions of users. The goal of this proposal is to develop an optimized, cost-effective, EEG sensor for home use. We propose an integrated approach to achieve cost-effectiveness and reliability by combining novel electrodes, amplifiers, Bluetooth transmission, and the battery on a single soft headband. SA1. Optimized EEG electrodes for reliable recording. Electrode design is the most critical element for high signal quality and a friendly user experience. We will test a number of novel self-adhesive electrodes inspired by gecko feet and grasshopper legs. These novel surfaces may bring large lateral grip force to stabilize the electrode over the skin, which may greatly reduce the artifact caused by relative movement between the skin and the electrode. SA2. Platform independent wireless transmission and data storage. We propose platform-independent Bluetooth wireless signal transmission to existing cellphones. As cellphones are widely used in the older and middle- aged population, recording and storage of EEG data on user's own cellphone is a cost-effective solution for large-scale use. We will use conventional voice recording APPs in every cellphone for data storage and a Bluetooth microphone for transmitting data from the headband to the user's cellphone. Such devices can be directly paired with cellphones running different operating systems without installation. Transmitting EEG through a voice band will be achieved with a frequency modulation circuit, and the EEG signals will be recovered from the voice file by a software demodulation program. Large scale measurement of sleep disruption depends on cost-effective solutions. Our project will not only contribute to the early detection/early intervention of AD pathology, but also serve as a research tool for researchers to collect large amounts of data to define early biomarkers of AD specific phenotypes.