Pattern recognition for the detection and monitoring of verbal and non-verbal alterations in Alzheimer's disease

用于检测和监测阿尔茨海默氏病言语和非言语改变的模式识别

• 批准号: RGPIN-2018-05714
• 负责人: Ratté, Sylvie
• 金额: $ 2.48万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=685330
• 关键词: Pattern; recognition; detection; monitoring; verbal

Alzheimer's disease (AD) affects one in ten adults over 65 in Canada. Interventions may be more effective in the early stages of dementia. Nevertheless, it is highly common, especially in low- and middle-income countries, to diagnose AD several years after the disease begins, leading to a treatment gap for early dementia sufferers. This gap could reduce the effectiveness of treatments, prolonging patients' state of reduced independence. Early diagnosis and treatment are now recognized as a means of attenuating care costs and reducing this gap. Furthermore, an early diagnosis would allow sufferers and their families to get their affairs in order by better anticipating the future and preparing accordingly.***The current program aims to automatically characterize and monitor changes in AD. This research focuses on analyzing verbal and non-verbal behaviours in multiple modalities: speech, discourse, and facial and corporal expressions. More specifically, we are pursuing three objectives:***1. Distinguish between AD patients and ailing patients using properties extracted from transcriptions and audio sources of conversations with elderly subjects (analysis of verbal behaviours).***2. Distinguish between AD patients and ailing patients using properties extracted from video recordings of conversations with elderly subjects (analysis of non-verbal behaviours).***3. Construct a machine learning system that will use every property obtained in (1) and (2) to identify various stages of AD.***To accomplish these tasks, we are using four recognized datasets: the BBVA of the BBVA Foundation in Spain, the DementiaBank Pitt Corpus from Pittsburgh University, the Carolina Conversations Collection from the Medical University of South Carolina, and the CorpAGEst corpus from Belgium (European Union Seventh Framework Program). The first two contain conversations made during the application of cognitive tests to patients. The last two contain natural free interviews in a non-stressful environment. The languages covered include Spanish, English and French.***The impacts of this proposal are twofold. First, it contributes to building stronger facial and gesture recognition techniques adapted to the aging population. That population being on the rise everywhere in the world, this contribution will allow the development of software aimed specifically at the elderly. Second, it contributes to developing automated tools to monitor AD and other types of dementia (e.g., the effects of medications on language abilities), giving the community of researchers tools to explore specificities in an automatic fashion. The results of this proposal are not meant to diagnose or to replace specialists (e.g., clinicians, doctors, geriatricians); they are aimed at assisting these professionals in innovative and non-invasive ways, helping them to understand the disease and its progression.

阿尔茨海默病（AD）影响加拿大65岁以上的十分之一的成年人。干预措施在痴呆症的早期阶段可能更有效。然而，在疾病开始几年后诊断AD非常常见，特别是在低收入和中等收入国家，导致早期痴呆症患者的治疗差距。这种差距可能会降低治疗的有效性，延长患者的独立性降低的状态。早期诊断和治疗现在被认为是减少护理费用和缩小这一差距的一种手段。此外，早期诊断将使患者及其家人通过更好地预测未来并相应地做好准备来安排他们的事务。目前的计划旨在自动表征和监测AD的变化。这项研究的重点是分析语言和非语言行为的多种形式：语音，话语，面部和身体表情。具体而言，我们正在追求三个目标：**1。使用从与老年受试者对话的transmittance和音频源中提取的属性来区分AD患者和患病患者（言语行为分析）。* 2.使用从与老年受试者对话的视频记录中提取的属性（非语言行为分析）区分AD患者和患病患者。* 3.构建一个机器学习系统，该系统将使用（1）和（2）中获得的每个属性来识别AD的各个阶段。为了完成这些任务，我们使用了四个公认的数据集：西班牙BBVA基金会的BBVA，匹兹堡大学的DementiaBank Pitt语料库，南卡罗来纳州医科大学的卡罗莱纳对话集，以及比利时的CorpAGEst语料库（欧盟第七框架计划）。前两个包含在对患者进行认知测试期间进行的对话。最后两个包含在无压力环境中的自然自由面试。所涉语文包括西班牙文、英文和法文。这一建议的影响是双重的。首先，它有助于建立适应老龄化人口的更强大的面部和手势识别技术。世界各地的人口都在增加，这一贡献将允许开发专门针对老年人的软件。其次，它有助于开发自动化工具来监测AD和其他类型的痴呆症（例如，药物对语言能力的影响），为研究人员提供了自动探索特异性的工具。本提案的结果并不意味着诊断或取代专家（例如，临床医生、医生、老年病学家）;这些活动旨在以创新和非侵入性的方式协助这些专业人员，帮助他们了解疾病及其进展。