Sleep metrics from machine learning for Alzheimer's disease diagnostics

用于阿尔茨海默病诊断的机器学习睡眠指标

基本信息

批准号：
10042952
负责人：
Joyita Dutta
金额：
$ 25.69万
依托单位：
UNIVERSITY OF MASSACHUSETTS LOWELL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10042952
关键词：
Accelerometer Address Affect Age Alleles Alzheimer disease detection Alzheimer&apos s Disease Alzheimer&apos s disease risk Apolipoproteins Artificial Intelligence Attention Biological Markers Characteristics Clinical Clinical Trials Cognition Cognitive Consensus Data Data Set Dementia Devices Diagnostic Disease Disease Management Early Diagnosis Early Intervention Early identification Elderly Electrocardiogram Electroencephalography Ensure Eye Funding Opportunities Genetic Goals Gold Grant Health Status Indicators Heart Rate Impaired cognition Individual Learning Literature Longitudinal Studies Machine Learning Measurement Measures Modality Modeling Monitor Motion Multi-Ethnic Study of Atherosclerosis National Institute on Aging Nature Neurobehavioral Manifestations Patients Pharmaceutical Preparations Phase Polysomnography Pulse Oximetry Research Design Route Series Sleep Sleep Disorders Sleep disturbances Symptoms Techniques Therapeutic Time Training United States National Institutes of Health Validation Wrist actigraphy aging brain autoencoder base carrier status cognitive ability cognitive performance cognitive testing cohort data resource deep learning drug candidate drug development high risk human subject improved long short term memory machine learning algorithm mild cognitive impairment normal aging novel pre-clinical predictive marker predictive modeling response secondary analysis sleep pattern sleep quality sociodemographic variables tool wearable device

项目摘要

PROJECT SUMMARY This proposal is responsive to NIH solicitation PA-17-089 for projects involving secondary analysis of pre-existing geriatric datasets. While presently there is no cure for Alzheimer’s disease, existing literature indicates that early diagnosis in the preclinical stage, i.e., before the onset of clinical symptoms, will be key to treatments. There is a pressing need for noninvasive predictors of cognitive decline that can enable early identification of individuals at Alzheimer’s disease risk. A mounting body of scientific evidence suggests that sleep disturbances (including microarchitectural disruptions to non-rapid-eye-motion sleep and decline in sleep quality) might be the earliest observable symptoms of Alzheimer’s disease. On-the-go sleep and activity monitoring could address the need for noninvasive indicators of cognitive decline in subjects who are in the (asymptomatic or mildly symptomatic) preclinical stage of Alzheimer’s disease. Here, we will build on preliminary results that reveal a set of sleep features derived from polysomnography (PSG) that are predictive of cognitive performance. We are proposing to perform secondary analysis of sleep and cognition data from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort using state-of-the-art deep learning tools to enable sleep-based prediction of cognitive impairment for early detection of Alzheimer’s disease. While PSG is the gold standard for sleep measurement, it is not well- suited for routine, day-to-day use. In comparison, wrist-based measurements (e.g. actigraphy, heart rate, ECG, and pulse oximetry) obtained from wearable devices allow “on-the-go” sleep monitoring. The combination of these on-the-go measures with the latest artificial intelligence tools is a feasible route to early Alzheimer’s diagnostics. We will use attention-guided long short-term memory autoencoders to identify overt and latent characteristics of the raw time-series datasets, which will allow us to more effectively mine the rich MESA data resource. Our deep learning framework will also take into account sociodemographic variables, indicators of health status, and medications. To ensure scientific rigor, secondary validation of the MESA-trained deep learning models will be performed on PSG and actigraphy data from the Harvard Aging Brain Study, which is a longitudinal study designed to further our understanding of what differentiates normal aging from preclinical Alzheimer’s disease. To address any concern about the “black-box” nature of deep learning models, we will compare the learned feature set with sleep microarchitectural features previously computed using classical statistical techniques. Previous data suggests that a subject’s apolipoprotein ε4 (ApoE4) allele carrier status influences the degree to which their sleep patterns impact their cognitive abilities. We will verify this by incorporating ApoE4 status as an additional input to the deep learning model. Literature shows that over 60% of patients with mild cognitive impairment and Alzheimer’s disease have at least one clinical sleep disorder. The on-the-go prediction paradigm using noninvasive sleep measurements to be validated in this project will have a significant impact on early Alzheimer’s diagnostics and facilitate ongoing clinical trials.

项目概要该提案是对 NIH 征集 PA-17-089 的响应，该征集涉及对现有物质进行二次分析的项目老年数据集虽然目前无法治愈阿尔茨海默病，但现有文献表明早期临床前阶段（即临床症状出现之前）的诊断将是治疗的关键。迫切需要认知能力下降的非侵入性预测因子，以便能够早期识别个体越来越多的科学证据表明，睡眠障碍（包括睡眠障碍）有患阿尔茨海默病的风险。非快速眼动睡眠的微结构破坏和睡眠质量下降）可能是最早的阿尔茨海默病的可观察症状可以满足移动睡眠和活动监测的需求。用于（无症状或轻度症状）受试者认知能力下降的无创指标在这里，我们将基于揭示一组睡眠的初步结果。我们提议从多导睡眠图 (PSG) 中提取可预测认知能力的特征。对多种族动脉粥样硬化研究 (MESA) 的睡眠和认知数据进行二次分析使用最先进的深度学习工具来基于睡眠预测认知障碍的队列虽然 PSG 是睡眠测量的黄金标准，但它的效果并不理想。相比之下，适合日常使用，基于手腕的测量（例如体动记录仪、心率、心电图、从可穿戴设备获得的数据（例如脉搏血氧仪）可以实现“移动”睡眠监测。这些使用最新人工智能工具的实时措施是治疗早期阿尔茨海默病的可行途径我们将使用注意力引导的长短期记忆自动编码器来识别显性和潜在性。原始时间序列数据集的特征，这将使我们能够更有效地挖掘丰富的 MESA 数据我们的深度学习框架还将考虑社会人口变量、指标。为确保科学性，对经过 MESA 培训的深度进行二次验证。学习模型将根据哈佛大脑老化研究的 PSG 和体动记录仪数据进行，该研究是一项纵向研究旨在进一步了解正常衰老与临床前衰老的区别为了解决对深度学习模型“黑匣子”性质的任何担忧，我们将将学习到的特征集与之前使用经典计算的睡眠微架构特征进行比较统计技术表明受试者的载脂蛋白ε4 (ApoE4) 等位基因携带状态。影响他们的睡眠模式影响他们的认知能力的程度，我们将通过以下方式验证这一点。将 ApoE4 状态作为深度学习模型的附加输入，文献表明超过 60% 的人。患有轻度认知障碍和阿尔茨海默病的患者至少患有一种临床睡眠障碍。在该项目中验证的使用无创睡眠测量的移动预测范式将具有对早期阿尔茨海默病的诊断产生重大影响，并促进正在进行的临床试验。