Longitudinal Speech Biomarkers for Automated Alzheimer's Detection
Longitudinal Speech Biomarkers for Automated Alzheimer's Detection
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DOI：
10.3389/fcomp.2021.624694
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发表时间：
2021-04-08
影响因子：
2.6
通讯作者：
Subirana, Brian
中科院分区：
文献类型：
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作者：
Laguarta, Jordi;Subirana, Brian
我们介绍了一种新的音频处理架构，开放语音脑模型（OVBM），提高检测精度阿尔茨海默氏症（AD）的纵向歧视自发语音。我们还概述了OVBM设计方法，使我们能够实现这种架构，这种架构通常可以结合多模式生物标志物，并同时针对多种疾病和其他AI任务。我们方法的关键是使用多种生物标志物相互补充，当其中两种生物标志物独特地识别目标疾病中的不同受试者时，我们说它们是正交的。我们通过引入16种生物标志物来说明OBVM设计方法，其中3种是正交的，证明了对两种明显不相关的疾病（如AD和COVID-19）的同时高于最先进的区分。根据上下文的不同，我们在整篇文章中使用OVBM来模糊地指代特定的架构或更广泛的设计方法。受麻省理工学院大脑与机器中心（CBMM）进行的研究的启发，OVBM结合了智能四个模块的生物标志物实现：大脑OS组块和重叠音频样本，并从感觉流和认知核心中聚合生物标志物特征，为目标任务创建符号组成模型的多模态图形神经网络。在本文中，我们将OVBM设计方法应用于阿尔茨海默氏痴呆症（AD）患者的自动诊断，仅使用原始音频即可实现93.8%的最新准确度，同时提取个性化的主题显着性图，旨在使用多种生物标志物纵向跟踪相对疾病进展，16在报告的AD任务中。最终目的是通过检测发病和治疗影响来帮助医疗实践，以便可以纵向测试干预方案。使用OBVM设计方法，我们引入了一种新的肺部和呼吸道生物标志物，该生物标志物使用200，000多个咳嗽样本创建，以预训练区分咳嗽文化起源的模型。随后，迁移学习被用于将该模型的功能整合到各种其他基于生物标记的OVBM架构中。在我们尝试的所有起始OBVM生物标志物架构组合中，该生物标志物在AD检测中产生一致的改善。这个咳嗽数据集设定了一个新的基准，成为最大的音频健康数据集，2020年4月有30，000多名受试者参与，首次展示了咳嗽文化偏见。
We introduce a novel audio processing architecture, the Open Voice Brain Model (OVBM), improving detection accuracy for Alzheimer's (AD) longitudinal discrimination from spontaneous speech. We also outline the OVBM design methodology leading us to such architecture, which in general can incorporate multimodal biomarkers and target simultaneously several diseases and other AI tasks. Key in our methodology is the use of multiple biomarkers complementing each other, and when two of them uniquely identify different subjects in a target disease we say they are orthogonal. We illustrate the OBVM design methodology by introducing sixteen biomarkers, three of which are orthogonal, demonstrating simultaneous above state-of-the-art discrimination for two apparently unrelated diseases such as AD and COVID-19. Depending on the context, throughout the paper we use OVBM indistinctly to refer to the specific architecture or to the broader design methodology. Inspired by research conducted at the MIT Center for Brain Minds and Machines (CBMM), OVBM combines biomarker implementations of the four modules of intelligence: The brain OS chunks and overlaps audio samples and aggregates biomarker features from the sensory stream and cognitive core creating a multi-modal graph neural network of symbolic compositional models for the target task. In this paper we apply the OVBM design methodology to the automated diagnostic of Alzheimer's Dementia (AD) patients, achieving above state-of-the-art accuracy of 93.8% using only raw audio, while extracting a personalized subject saliency map designed to longitudinally track relative disease progression using multiple biomarkers, 16 in the reported AD task. The ultimate aim is to help medical practice by detecting onset and treatment impact so that intervention options can be longitudinally tested. Using the OBVM design methodology, we introduce a novel lung and respiratory tract biomarker created using 200,000+ cough samples to pre-train a model discriminating cough cultural origin. Transfer Learning is subsequently used to incorporate features from this model into various other biomarker-based OVBM architectures. This biomarker yields consistent improvements in AD detection in all the starting OBVM biomarker architecture combinations we tried. This cough dataset sets a new benchmark as the largest audio health dataset with 30,000+ subjects participating in April 2020, demonstrating for the first time cough cultural bias.