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）是一种严重的国民健康问题 2020 年，这一担忧影响了 580 万人，预计比 2020 年增加 40% 有证据表明，功能性、心理性、病理性和 ADRD 潜在的生理变化可能在临床出现之前许多年就出现 认知症状的表现，这增加了早期检测的兴趣 和监测，为个人和家庭的疾病预测和管理提供信息 此外，晚年抑郁症发病率较高与年龄有关。 与 ADRD 相关的听力损失在很长一段时间内复杂的治疗 鉴于需要改进措施来了解和治疗 ADRD，有几种疾病。 国家老龄化研究所的各个部门呼吁改进方法 与衰老相关的认知衰退的预后、诊断和/或治疗监测 对早期认知变化更敏感，成本更低且无创。 听力保健、语音分析和机器学习方面数字健康的进步 提供了巨大的机会来提供具有成本效益、用户友好的认知 对于生活在偏远地区、城市和地区的人们来说，可以很容易地使用或调整的措施 城郊社区的助听器 (HA) 具有数字信号处理功能， 语音分析所需的计算和无线通信能力 HA 自身语音检测的独特能力有助于分析任务。 语音的非语义副语言声学特征表明早期变化 通过 HA 提取语音非语义特征的能力是一种能力。 维护临床或结构化之外的用户隐私的关键方面 对话，即在人的正常日常生活活动期间。