Developing an imperceptible electroencephalography at-home sleep monitoring system for cognitive and clinical research

项目摘要。睡眠障碍在美国很普遍， 疾病控制和预防中心（CDC）的健康流行病。睡眠不好影响深远 对认知，健康，并有助于增加许多疾病的风险，包括阿尔茨海默病（AD）。 睡眠期间测量的脑电图（EEG）神经特征可能对AD神经病理学敏感 在AD诊断前几年，它可以预测认知正常的人的认知能力下降。目前，AD 病理学只能使用PET成像或通过侵入性且昂贵的脊椎穿刺在体内测量。 如果某些睡眠EEG特征被认为对AD病理学是敏感和特异的，它们可以作为早期， 这种疾病的非侵入性诊断生物标志物。睡眠EEG信号传统上是使用体积大， 在不熟悉的实验室环境中使用坚硬、不舒适的设备，可能会对睡眠信号产生负面影响。由于这些 由于睡眠EEG数据采集的局限性，睡眠EEG数据采集通常限于单个夜晚。由于夜间睡眠的波动性 如果预测认知表现和健康结果不佳，就必须对多晚的睡眠进行采样。 现有的移动的系统仍然体积庞大，并且缺乏可靠地评估睡眠阶段所需的电极。多PI 跨学科团队，在衰老认知神经科学、机械/生物医学 工程和睡眠神经科学将开发和验证一种低成本，几乎察觉不到，柔软，可穿戴， 无线移动终端，设计用于长期使用，并使用它来测量个人的睡眠脑电图，因为他们睡在 在自己的家中度过了多个夜晚。这个设备是第一个完全便携的，紧凑的，灵活的皮肤的例子， 像混合电子（SKINTRONICS）EEG系统，包括雾化喷射打印的前额，眼睛和 下巴运动电极，锂聚合物电池，以及用于数据存储的微型安全数字（SD）卡。的 封装器件的弹性体可防止干扰，并确保高SNR，而无需 电极凝胶EEG记录的所有必要组件大致包含在非粘性粘合剂中 创可贴的大小，参与者可以很容易地应用和删除自己。将对该系统进行评估 在实验室和家中，在年轻人和老年人中，以确保在具有潜在不同水平的成人中的可行性。 可穿戴技术的EEG SNR、睡眠质量和舒适度。个体水平之间的关系 对睡眠质量高度敏感的情景记忆任务的睡眠EEG指标和表现 和AD将被评估，以确认我们的系统揭示了睡眠EEG-记忆关联 与文献中的结果一致。将采集10个晚上的数据，以长期测试SKINTRONICS 能力，以便将睡眠EEG中的习惯性夜间变异性的预测值 内存性能的信号。此应用程序产生的结果还将适用于 在有AD风险的参与者中进行的纵向研究，以确定特定的睡眠EEG模式， 使用SKINTRONICS，可以帮助识别最有可能表现出认知能力下降的个体。

PROJECT SUMMARY. Disordered sleep is pervasive in the United States and has been declared a public health epidemic by the Centers for Disease Control and Prevention (CDC). Poor sleep has far-reaching impacts on cognition, health, and contributes to an increased risk of many diseases, including Alzheimer’s disease (AD). Electroencephalography (EEG) neural signatures measured during sleep may be sensitive to AD neuropathology that is predictive of cognitive decline in cognitively normal people years before AD diagnosis. Currently, AD pathology can only be measured in vivo using PET imaging or via spinal taps, which are invasive and expensive. If certain sleep EEG signatures are deemed to be sensitive and specific to AD pathology, they may serve as early, non-invasive, diagnostic biomarkers of the disease. Sleep EEG signals are traditionally measured using bulky, rigid, uncomfortable equipment in an unfamiliar lab setting that can negatively impact sleep signals. Due to these limitations, sleep EEG data acquisition is typically limited to a single night. As night-to-night sleep variability is predictive of poor cognitive performance and health outcomes, it is essential to sample multiple nights of sleep. Existing mobile systems are still bulky and lack electrodes needed to reliably assess sleep stages. The multi-PI interdisciplinary team with complementary expertise in cognitive neuroscience of aging, mechanical/biomedical engineering, and sleep neuroscience will develop and validate a low-cost, virtually imperceptible, soft, wearable, wireless mobile device designed for long-term use and use it to measure individuals’ sleep EEG as they sleep in their own homes over multiple nights. This device is the first example of a fully portable, compact, flexible skin- like hybrid electronics (SKINTRONICS) EEG system that includes ultrathin aerosol-jet printed forehead, eye and chin movement electrodes, a Li-polymer battery, and a micro secure digital (SD) card for data storage. The elastomer that encapsulates the device protects from interference and ensures high SNR without the need for electrode gel. All necessary components for EEG recording are contained within a non-sticky adhesive roughly the size of a Band-Aid® that participants can easily apply and remove themselves. This system will be assessed in the lab and at home in young and older adults to ensure feasibility in adults with potentially different levels of EEG SNR, sleep quality, and comfort with wearable technology. The relationship between individual levels of sleep EEG metrics and performance on an episodic memory task designed to be highly sensitive to sleep quality and AD will be assessed for the purposes of confirming that our system reveals sleep EEG-memory associations consistent with those in the literature. Ten nights of data will be acquired to test SKINTRONICS long-term capabilities and in order to separate the predictive value of habitual from night-to-night variability in sleep EEG signals on memory performance. Results produced by this application will additionally be applicable for longitudinal studies in participants at risk for AD to determine whether particular sleep EEG patterns, acquired with SKINTRONICS, can help identify individuals most likely to exhibit cognitive decline.