Early detection and monitoring of Alzheimers Disease and Related Dementias using non-semantic linguistic and acoustic features of speech derived from hearing aids

使用助听器语音的非语义语言和声学特征早期检测和监测阿尔茨海默病和相关痴呆症

• 批准号: 10600233
• 负责人: Brian John Bischoff
• 金额: $ 26.58万
• 依托单位: HEADWATERS INNOVATION, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10600233
• 关键词: Acoustics; Activities of Daily Living; Affect; Aging; Alzheimer' s disease related dementia; Area; Bluetooth; Caring; Cellular Phone; Classification; Clinical; Cognitive; Communication; Data; Data Collection; Dementia; Detection; Diagnosis; Digital Signal Processing; Disease; Early Diagnosis; Early identification; Framingham Heart Study; Generations; Health; Hearing; Hearing Aids; Impaired cognition; Individual; Label; Linguistics; Machine Learning; Measures; Methodology; Methods; Monitor; National Institute on Aging; Neurobehavioral Manifestations; Neuropsychology; Participant; Pathologic; Persons; Phase; Physiological; Population; Presbycusis; Prevention; Privacy; Prognosis; ROC Curve; Research; Research Personnel; Sampling; Semantics; Source; Speech; Structure; Technology; Training; Urban Community; Voice; cloud based; cognitive change; cognitive performance; cost; cost effective; demographics; digital health; experience; field study; geriatric depression; high risk; illness length; improved; interest; machine learning model; mild cognitive impairment; overtreatment; peri-urban; psychologic; remote sensor; task analysis; user-friendly; wireless communication

Abstract Alzheimer’s disease and related dementias (ADRD) are a serious national health concern that affected 5.8 million in 2020 and are expected to increase by 40% over the next decade. There is evidence that the functional, psychological, pathological, and physiological changes underlying ADRD may emerge many years prior to the clinical manifestation of cognitive symptoms, which is increasing the interest in early detection and monitoring to inform disease prediction and management at both the individual and population level. In addition, the higher rates of late-life depression and age-related hearing loss associated with ADRD complicate treatment over the long duration of the disease. Given the need for improved measures to understand and treat ADRD, several divisions of the National Institute of Aging have called for improved methodologies for prognosis, diagnosis and/or treatment monitoring of aging related cognitive decline that are more sensitive to early cognitive changes, less costly and noninvasive. Advances in digital health for hearing care, speech analysis and machine learning present tremendous opportunities to provide cost-effective, user-friendly cognitive measures that can be readily used, or adapted, for persons living in remote, urban, and peri-urban communities. The hearing aids (HAs) have the digital signal processing, computational and wireless communication capabilities needed for speech-analysis tasks. The unique ability of the HA for own voice detection facilitates the analysis of non-semantic paralinguistic acoustic features of speech indicative of early changes in cognitive health. The ability to extract non-semantic features of voice through the HA is a key aspect of maintaining privacy for the user outside of clinical or structured conversations, i.e. during the person’s normal activities of daily living.

抽象的 阿尔茨海默氏病和相关痴呆症（ADRD）是一种严重的国家健康 担心在2020年影响580万，预计将增长40％ 接下来的十年。有证据表明功能，心理，病理和 在临床之前，ADRD的身体变化可能会出现多年 认知症状的表现，这增加了对早期检测的兴趣 并监视以告知个人的疾病预测和管理 人口水平。此外，晚期抑郁症和与年龄有关的率较高 与ADRD复杂治疗相关的听力损失在长时间的持续时间内 疾病。考虑到需要改进的措施来理解和治疗ADRD，有几个 美国国家老化研究所的划分呼吁改进方法 衰老相关认知下降的预后，诊断和/或治疗监测 对早期认知变化更敏感，成本较小和无创。 听力护理，语音分析和机器学习的数字健康进步 目前的巨大机会可以提供具有成本效益，用户友好的认知 可以轻松使用或适应居住在偏远，城市和 城市周边社区。助听器（HAS）具有数字信号处理， 语音分析所需的计算和无线通信功能 任务。 HA对自己的语音检测的独特能力有助于分析 语音的非语义阶段性声学特征，表明早期变化 认知健康。通过HA提取语音的非语义特征的能力是 在临床或结构化之外为用户保持隐私的关键方面 对话，即在此人的日常生活正常活动中。