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

具有成本效益的脑电图传感器，用于监测阿尔茨海默病早期阶段的睡眠中断

• 批准号: 10478859
• 负责人: Jian-Young Wu
• 金额: $ 19.5万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10478859
• 关键词: Adhesives; Adopted; Affect; Alleles; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease patient; Alzheimer' s disease risk; Alzheimer’s disease biomarker; Amplifiers; Arousal; Biological Markers; Bluetooth; Body measure procedure; Computer software; Computers; Data; Data Storage and Retrieval; Dementia; Development; Devices; Early Diagnosis; Early Intervention; Electrodes; Electroencephalography; Elements; Frequencies; Goals; Grasshoppers; Hair; Head; Health Care Costs; Home; Impaired cognition; Individual; Intervention; Knowledge; Lateral; Leg; Location; Measurement; Monitor; Morphologic artifacts; Movement; Neurobehavioral Manifestations; Obstructive Sleep Apnea; Operating System; Patient Care; Pattern; Periodicity; Persons; Phenotype; Polysomnography; Population; Public Domains; REM Sleep; Research; Research Personnel; Risk Factors; Running; Side; Signal Transduction; Skin; Sleep; Sleep Architecture; Sleep Stages; Sleep disturbances; Structure; Surface; System; Testing; Traction; Voice; Wakefulness; abeta accumulation; actigraphy; aging population; apolipoprotein E-4; base; cost; cost effective; cost effectiveness; density; design; early detection biomarkers; effective intervention; electric impedance; experience; foot; grasp; human subject; microphone; middle age; next generation; non rapid eye movement; novel; platform-independent; pre-clinical; programs; rapid eye movement; risk variant; sensor; specific biomarkers; success; tool; transmission process; user-friendly; wireless; wireless transmission

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.

项目概要/摘要 睡眠中断影响25-40%的轻度至中度痴呆的阿尔茨海默病（AD）患者。 与阻塞性睡眠呼吸暂停不同，睡眠结构的破坏是与早期睡眠呼吸暂停高度相关的生物标志物。 AD分期和APOE e4等位基因危险因素。测量身体运动的睡眠传感器（体动仪）无法检测到 在非快速眼动（NREM）和快速眼动（REM）睡眠阶段之间转换的周期性模式。 睡眠结构的准确监测需要脑电图（EEG）记录。家用EEG传感器 但是，由于价格昂贵，而且日常穿着也不舒适，因此它们远非理想。仍需付出巨大努力 朝向电极、无线信号传输和总体成本效益的改进。成本低， 易于使用是公众接受数百万用户的大规模测量的必要因素。的 本提案的目标是开发一种优化的、具有成本效益的家用EEG传感器。 我们提出了一种综合方法，通过结合新型电极， 放大器，蓝牙传输，和电池上的一个单一的软头带。 SA 1.优化的EEG电极，用于可靠记录。电极设计是最关键的因素， 信号质量和友好的用户体验。我们将测试一些受壁虎启发的新型自粘电极 脚和蚱蜢腿。这些新颖的表面可以带来大的横向夹持力以将电极稳定在皮肤上， 这可以极大地减少由皮肤和电极之间的相对运动引起的伪影。 SA 2.独立于平台的无线传输和数据存储。我们建议平台无关 蓝牙无线信号传输到现有的手机。随着手机在中老年人中的广泛使用， 对于老年人来说，在用户自己的手机上记录和存储EEG数据是大规模的具有成本效益的解决方案。 使用.我们将在每部手机中使用传统的语音记录应用程序来存储数据，并使用蓝牙麦克风来 将数据从头带传输到用户的手机。这种设备可以直接与手机配对 运行不同的操作系统而无需安装。通过语音带传输EEG将通过 频率调制电路，并将脑电信号从语音文件中恢复出来，通过软件解调 程序. 睡眠中断的大规模测量取决于具有成本效益的解决方案。我们的项目不仅 有助于AD病理的早期检测/早期干预，但也可作为研究人员的研究工具， 收集大量数据以定义AD特异性表型的早期生物标志物。