摘要
背景:各种类型的痴呆和轻度认知功能障碍(MCI)表现为人类言语和语言的不规则性,已被证明是疾病存在和进展的有力预测因子。因此,由移动应用程序提供的自动语音分析可能是提供用于评估和检测早期痴呆和MCI的额外指标的有用工具。 方法:165名受试者(主观认知障碍(SCI),MCI患者,阿尔茨海默病(AD)和混合型痴呆(MD)患者)在移动应用程序中进行记录,同时在定期咨询期间执行几个短的声音认知任务。这些任务包括口头流利,图片描述,倒计时和言论自由任务。录音分两步处理:第一步,使用语音信号处理技术提取声音标记;在第二,声乐标记进行了测试,以评估他们的“权力”区分SCI,MCI,AD和MD。第二步包括基于机器学习方法训练用于检测MCI和AD的自动分类器,并测试检测精度。 结果:流利性和言语自由度的任务获得最高的准确率分类AD与MD对MCI与SCI。使用这些数据,我们证明了分类准确性如下:SCI与AD = 92%的准确性; SCI与MD = 92%的准确性; SCI对MCI = 86%的准确性和MCI对AD = 86%。 结论:我们的研究结果表明了声音分析的潜在价值和使用移动应用程序准确自动区分SCI,MCI和AD。该工具可以为临床医生提供有意义的信息,以非侵入性,简单和低成本的方法基于MCI和AD对患者进行评估和监测。
关键词: 阿尔茨海默病,痴呆,评估,MCI,语音,音频分析,机器学习,算法。
Current Alzheimer Research
Title:Use of Speech Analyses within a Mobile Application for the Assessment of Cognitive Impairment in Elderly People
Volume: 15 Issue: 2
关键词: 阿尔茨海默病,痴呆,评估,MCI,语音,音频分析,机器学习,算法。
摘要: Background: Various types of dementia and Mild Cognitive Impairment (MCI) are manifested as irregularities in human speech and language, which have proven to be strong predictors for the disease presence and progress ion. Therefore, automatic speech analytics provided by a mobile application may be a useful tool in providing additional indicators for assessment and detection of early stage dementia and MCI.
Method: 165 participants (subjects with subjective cognitive impairment (SCI), MCI patients, Alzheimer's disease (AD) and mixed dementia (MD) patients) were recorded with a mobile application while performing several short vocal cognitive tasks during a regular consultation. These tasks included verbal fluency, picture description, counting down and a free speech task. The voice recordings were processed in two steps: in the first step, vocal markers were extracted using speech signal processing techniques; in the second, the vocal markers were tested to assess their ‘power' to distinguish between SCI, MCI, AD and MD. The second step included training automatic classifiers for detecting MCI and AD, based on machine learning methods, and testing the detection accuracy.
Results: The fluency and free speech tasks obtain the highest accuracy rates of classifying AD vs. MD vs. MCI vs. SCI. Using the data, we demonstrated classification accuracy as follows: SCI vs. AD = 92% accuracy; SCI vs. MD = 92% accuracy; SCI vs. MCI = 86% accuracy and MCI vs. AD = 86%.
Conclusions: Our results indicate the potential value of vocal analytics and the use of a mobile application for accurate automatic differentiation between SCI, MCI and AD. This tool can provide the clinician with meaningful information for assessment and monitoring of people with MCI and AD based on a non-invasive, simple and low-cost method.
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Cite this article as:
Use of Speech Analyses within a Mobile Application for the Assessment of Cognitive Impairment in Elderly People, Current Alzheimer Research 2018; 15 (2) . https://dx.doi.org/10.2174/1567205014666170829111942
DOI https://dx.doi.org/10.2174/1567205014666170829111942 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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