摘要
目的:这项研究提出了一种新的方法来早期发现老年人的认知障碍。该方法结合使用语音分析,多元统计和数据挖掘技术。我们已经开发了一种基于言语韵律的认知障碍评定(SPCIR),其使用从老年人言语提取的韵律信号来区分认知正常对照和具有轻度阿尔茨海默病(mAD)或轻度认知障碍(MCI)的老年人,同时进行问卷调查。 273名日本人(男性73人,女性200人,年龄在65-96岁之间)参加了这项研究。作者在修订后的Hasegawa痴呆量表(HDS-R)考试期间收集了对话片段的语音,并讨论了与家乡,童年和学校有关的话题。这些片段对应于关于出生日期(T1),受试者小学名称(T2),时间定向(Q2)和向后重复三位数字(Q6)的问题的答案中的语音。从每个语音中提取尽可能多的韵律特征,包括基频,共振峰,强度特征和梅尔频率倒谱系数。他们使用主成分分析和/或特征选择进行了细化。作者使用多元线性回归分析计算了SPCIR。 结论:此外,本研究提出了一个二元歧视模型的SPCIR使用多变量逻辑回归和模型选择与接受者操作特征曲线分析和报告灵敏度和特异性SPCIR的诊断(控制与MCI / mAD)。这项研究也报告有歧视性的表现,从而表明提出的方法可能是一个有效的工具,筛选老人的mAD和MCI。
关键词: 早期发现痴呆症,MCI,轻度阿尔茨海默病,基于言语韵律的认知障碍评分。
Current Alzheimer Research
Title:Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech
Volume: 15 Issue: 2
关键词: 早期发现痴呆症,MCI,轻度阿尔茨海默病,基于言语韵律的认知障碍评分。
摘要: 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.
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Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech, Current Alzheimer Research 2018; 15 (2) . https://dx.doi.org/10.2174/1567205014666171120144343
DOI https://dx.doi.org/10.2174/1567205014666171120144343 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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