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

预计到2040年，英国痴呆症的患病率将翻一番，是目前英国经济年成本347亿GB的三倍。痴呆症是一种认知功能的进行性下降，包括记忆、执行和视觉空间功能的问题。阿尔茨海默病(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，但这可能很难与正常的认知老化区分开来，并不总是被识别或诊断。然而，早期诊断是至关重要的，因为当出现主要认知症状时，可能会发生相当大的皮质损伤，限制了治疗选择。未来痴呆症的一些迹象，如步态和言语障碍，可能在严重认知能力下降开始前几年出现。这为智能手机、可穿戴设备和家庭监控等数字表型技术提供了机会，有助于及早发现和管理AD和MCI。布里斯托尔大学的SPHERE系统是未来健康智能家居的研究试验台。它使用包括视频和音频在内的多个连续传感平台，以及来自可穿戴传感器的加速计和生理数据，来监控步态、导航、日常生活活动(ADL)和社交等健康行为。长方体项目旨在将标准的临床神经心理测试与SPERE的多模式数据相结合，以得出早期认知下降的数字信号的基本事实。长方体由医学研究委员会资助，并获得威尔士REC7委员会的伦理批准。尽管新冠肺炎带来了颠覆，但长方体非常适合在线数据采集。事实上，居家演示模式排除了去诊所的要求，并可以减少患者的焦虑，这可以将变异性引入测量数据。本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；以及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已经在使用一种平板电脑部署的、受Gogglebox启发的谈话任务来收集语音数据，参与者可以讨论他们喜欢的电视节目。结合标准的语言临床测试，这项自然主义的说话任务有可能确定语音特征(例如，韵律或语言补偿)的可变性如何与ADL的变化相关，如早期MCI和AD的导航或家庭活动。它还可能显示，语音的细微变化是否先于ADL的变化，这在MCI和早期AD之间可能有何不同--并可能形成MCI或AD的预测性诊断工具。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及其与语言刺激的关系进行文献综述。除了对第一年的现有数据进行分析外，一项可行性研究将研究如何在获得赞助商(UOB)和HRA的适当批准的情况下，使用当前基于平板的平台将谈话任务评估推向在线格式。然后将从年龄-性别匹配的对照组(需要UOB REC批准)收集标准数据，并将其与病前智力测试(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和语音模式的基本事实之间的关联。除了写我的论文，在第三年，我将进行一项研究，以确定基于被动语音采样的干预措施的可行性，这些干预措施可以缓解症状或促进护理。

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.