Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech
Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech
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DOI：
10.2174/1567205014666171120144343
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发表时间：
2018-01-01
影响因子：
2.1
通讯作者：
Sakuma, Takuto
中科院分区：
文献类型：
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作者：
Kato, Shohei;Homma, Akira;Sakuma, Takuto
目的：提出一种早期检测老年人认知功能障碍的新方法。该方法结合了语音分析、多元统计和数据挖掘技术的使用。我们开发了一种基于语音韵律的认知障碍评分（SPCIR），可以通过从老年人语音中提取的韵律信号来区分认知正常对照和患有轻度阿尔茨海默病（mAD）或轻度认知障碍（MCI）的老年人。273名日本受试者（73名男性和200名女性，年龄在65岁至96岁之间）参加了这项研究。作者收集了在修订后的长谷川痴呆量表（HDS-R）检查中对话片段的语音，以及与家乡、童年和学校有关的话题。这些片段对应于回答有关出生日期（T1）、小学名称（T2）、时间方向（Q2）和向后重复三位数（Q6）等问题的语音。从每个语音中提取尽可能多的韵律特征，包括基频、形成峰、强度特征和梅尔-频率倒谱系数。使用主成分分析和/或特征选择对它们进行细化。作者利用多元线性回归分析计算了SPCIR。结论：此外，本研究采用多变量logistic回归和模型选择，结合受试者工作特征曲线分析，提出了SPCIR的二元判别模型，并报告了SPCIR对诊断的敏感性和特异性（对照与MCI/mAD）。该研究还报告了良好的判别性能，从而表明所提出的方法可能是筛查老年人mAD和MCI的有效工具。
Objective: This study presents a novel approach for early detection of cognitive impairment in the elderly. The approach incorporates the use of speech sound analysis, multivariate statistics, and data-mining techniques. We have developed a speech prosody-based cognitive impairment rating (SPCIR) that can distinguish between cognitively normal controls and elderly people with mild Alzheimer's disease (mAD) or mild cognitive impairment (MCI) using prosodic signals extracted from elderly speech while administering a questionnaire. Two hundred and seventy-three Japanese subjects (73 males and 200 females between the ages of 65 and 96) participated in this study. The authors collected speech sounds from segments of dialogue during a revised Hasegawa's dementia scale (HDS-R) examination and talking about topics related to hometown, childhood, and school. The segments correspond to speech sounds from answers to questions regarding birthdate (T1), the name of the subject's elementary school (T2), time orientation (Q2), and repetition of three-digit numbers backward (Q6). As many prosodic features as possible were extracted from each of the speech sounds, including fundamental frequency, formant, and intensity features and mel-frequency cepstral coefficients. They were refined using principal component analysis and/or feature selection. The authors calculated an SPCIR using multiple linear regression analysis.Conclusion: In addition, this study proposes a binary discrimination model of SPCIR using multivariate logistic regression and model selection with receiver operating characteristic curve analysis and reports on the sensitivity and specificity of SPCIR for diagnosis (control vs. MCI/mAD). The study also reports discriminative performances well, thereby suggesting that the proposed approach might be an effective tool for screening the elderly for mAD and MCI.