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: 10213321
• 负责人: Jian-Young Wu
• 金额: $ 23.4万
• 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 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10213321
• 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; 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; Wireless Technology; 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

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.

项目摘要/摘要 睡眠破坏会影响25-40％的阿尔茨海默氏病（AD）轻度至中度痴呆症患者。 与阻塞性睡眠呼吸暂停不同的睡眠结构中断是一种与早期相关的生物标志物 AD和APOE E4等位基因风险因素的阶段。测量身体运动的睡眠传感器无法检测到 周期性的模式在非比型眼运动（NREM）和快速眼动（REM）睡眠阶段之间移动。 对睡眠体系结构的准确监控需要脑电图（EEG）记录。基于家庭的脑电图传感器 远非理想，因为它们每天都不舒服，而且不舒服。仍然需要大量努力 朝着改善电子，无线信号传输和整体成本效益。低成本和 易于使用是公众接受数百万用户的大规模测量所必需的重要因素。 该提案的目标是开发一种优化的，具有成本效益的脑电图传感器，以供家庭使用。 我们提出了一种综合方法，以结合新型电子，以实现成本效益和可靠性， 放大器，蓝牙变速箱和单个软头带上的电池。 SA1。优化的EEG电极可靠记录。电极设计是高的最关键元素 信号质量和友好的用户体验。我们将测试受gecko启发的许多新型自粘电极 脚和蚱hopper腿。这些新颖的表面可能会带来大的侧向握力，以稳定皮肤上的电极， 这可能会大大减少由皮肤和电极之间的相对运动引起的伪影。 SA2。平台独立的无线传输和数据存储。我们建议独立于平台 蓝牙无线信号传输到现有手机。由于手机被广泛用于较旧的和中间 老年人口，在用户自己的手机上记录和存储脑电图数据是大规模的成本效益的解决方案 使用。我们将在每个手机中使用常规语音录制应用程序进行数据存储，并使用蓝牙麦克风 将数据从头带传输到用户的手机。这样的设备可以直接与手机配对 在没有安装的情况下运行不同的操作系统。通过一个通过语音乐队传输脑电图将通过 频率调制电路和EEG信号将通过软件解调从语音文件中恢复 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。我们的项目不仅会 为AD病理学的早期检测/早期干预做出贡献，但也是研究人员的研究工具 收集大量数据以定义AD特定表型的早期生物标志物。