Establishing computational behavioural models for the detection of early dementia from speech

建立计算行为模型以从言语中检测早期痴呆症

• 批准号: 2275613
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2275613
• 关键词: Establishing; computational; behavioural; models; detection

The prevalence of dementia in the UK is projected to double by 2040, trebling the current annual cost to the UK economy of £34.7 billion. Dementia is a progressive decline in cognitive function, including problems with memory, executive, and visuospatial functions. Alzheimer's Disease (AD) is the most common cause of dementia and all AD patients transition through an early phase of mild cognitive impairment (MCI), but this can be difficult to distinguish from normal cognitive ageing and is not always recognised or diagnosed. However, early diagnosis is crucial because considerable cortical damage can occur by the time major cognitive symptoms are exhibited, limiting treatment options. Some signs of future dementia, such as gait and speech disturbances, can present years before the onset of serious cognitive decline. This presents opportunities for digital phenotyping technologies such as smartphones, wearables and in-home monitoring to help in early detection and management of AD and MCI. The SPHERE system at Bristol University is a research test bed for the health-enabled smart home of the future. It uses multiple continuous sensing platforms including video and audio, as well as accelerometer and physiological data from wearable sensors, to monitor health behaviours such as gait, navigation, activities of daily living (ADL) and social contact. The CUBOiD project aims to combine standard clinical neuropsychological testing with mulitmodal data from SPHERE to derive ground truths on digital signals of early cognitive decline. CUBOiD is funded by the Medical Research Council and has ethical approval from the Wales REC7 committee. Despite the disruption caused by COVID-19, CUBOiD is well-suited to adaptation for online data acquisition. Indeed, an in-home mode of presentation precludes the requirement to attend clinic and could reduce patient anxiety, which can introduce variability into measured data.The goals of this PhD research project are 1) to develop novel machine learning models that are informed by same-patient data on ADL and can detect changes in speech as diagnostic signatures of early AD and MCI; and 2) to develop novel digital interventions that may alleviate symptoms and slow disease progression. CUBOiD has already been collecting speech data using a tablet-deployed, Gogglebox-inspired talking task in which participants discuss a TV show they enjoy. Combined with standard linguistic clinical tests, this naturalistic speaking task has potential to identify how variability in speech features (e.g., prosody or linguistic compensations) may correlate with changes in ADL such as navigation or household activities in early MCI and AD. It could potentially also show whether subtle changes in speech precede changes in ADL, how this may differ between MCI and early AD- and could form a predictive diagnostic tool for MCI or AD. Over the summer of 2020 I will conduct a literature review of linguistic anomalies seen in MCI and AD, and of digital therapies and how they relate to linguistic stimulation. Alongside analysis of existing data in year 1, a feasibility study will examine how to push the talking task assessments into an online format using the current tablet-based platform with appropriate approvals from the Sponsor (UoB) and the HRA. Normative data will then be collected from age-sex matched controls (UoB REC approval required) and compared with performance on premorbid intelligence tests (such as NART-IQ) to establish how they relate to educational level in people without dementia. In year 2, I will collect speech task data from people with a diagnosis of MCI and AD to establish correlations with ADL and ground truths on speech patterns. Alongside writing up my thesis, in year 3, I will conduct a study to establish the feasibility of interventions that could alleviate symptoms or facilitate care based on passive sampling of speech.

预计到2040年，英国痴呆症的患病率将翻一番，是目前英国经济每年347亿英镑成本的三倍。痴呆是认知功能的进行性衰退，包括记忆、执行和视觉空间功能的问题。阿尔茨海默氏病（AD）是痴呆症最常见的原因，所有AD患者都会经历轻度认知障碍（MCI）的早期阶段，但这可能难以与正常的认知老化区分开来，并且并不总是被识别或诊断。然而，早期诊断是至关重要的，因为在出现主要认知症状时可能会发生相当大的皮质损伤，从而限制了治疗选择。未来痴呆症的一些迹象，如步态和语言障碍，可能在严重认知能力下降的发病前几年出现。这为智能手机、可穿戴设备和家庭监测等数字表型技术提供了机会，以帮助早期检测和管理AD和MCI。布里斯托大学的SPHERE系统是未来健康智能家居的研究试验台。它使用多个连续传感平台，包括视频和音频，以及来自可穿戴传感器的加速度计和生理数据，以监测步态，导航，日常生活活动（ADL）和社交接触等健康行为。CUBOiD项目旨在将联合收割机标准临床神经心理学测试与SPHERE的多模态数据相结合，以获得早期认知衰退数字信号的基本事实。CUBOiD由医学研究理事会资助，并获得威尔士REC 7委员会的伦理批准。尽管COVID-19造成了中断，CUBOiD非常适合在线数据采集。事实上，在家里的演示模式排除了参加诊所的要求，并可以减少患者的焦虑，这可能会导致测量数据的变化。这个博士研究项目的目标是1）开发新的机器学习模型，该模型由同一患者的ADL数据提供信息，并可以检测语音变化作为早期AD和MCI的诊断特征; 2）开发新的数字干预措施，可以缓解症状和减缓疾病进展。CUBOiD已经在使用平板电脑收集语音数据，Gogglebox启发了谈话任务，参与者讨论他们喜欢的电视节目。结合标准的语言学临床测试，这种自然主义的说话任务有可能识别语音特征的变化（例如，韵律或语言补偿）可能与早期MCI和AD中ADL的变化（如导航或家庭活动）相关。它还可能显示言语的细微变化是否先于ADL的变化，MCI和早期AD之间的差异，并可能形成MCI或AD的预测诊断工具。在2020年夏天，我将对MCI和AD中出现的语言异常以及数字疗法以及它们与语言刺激的关系进行文献综述。除了分析第1年的现有数据外，可行性研究还将研究如何使用当前基于平板电脑的平台将谈话任务评估推向在线格式，并获得申办者（UoB）和HRA的适当批准。然后将从年龄性别匹配的对照组（需要UoB REC批准）中收集规范性数据，并与病前智力测试（如NART-IQ）的表现进行比较，以确定它们与非痴呆症患者的教育水平之间的关系。在第二年，我将收集诊断为MCI和AD的人的语音任务数据，以建立与ADL和语音模式的地面事实的相关性。除了写我的论文，在第三年，我将进行一项研究，以确定干预措施的可行性，这些干预措施可以缓解症状或促进基于语音被动采样的护理。