BACKGROUND Early detection of mild cognitive impairment is crucial in the prevention of Alzheimer's disease. The aim of the present study was to identify whether acoustic features can help differentiate older, independent community-dwelling individuals with cognitive impairment from healthy controls. METHODS A total of 8779 participants (mean age 74.2 ± 5.7 in the range of 65-96, 3907 males and 4872 females) with different cognitive profiles, namely healthy controls, mild cognitive impairment, global cognitive impairment (defined as a Mini Mental State Examination score of 20-23), and mild cognitive impairment with global cognitive impairment (a combined status of mild cognitive impairment and global cognitive impairment), were evaluated in short-sentence reading tasks, and their acoustic features, including temporal features (such as duration of utterance, number and length of pauses) and spectral features (F0, F1, and F2), were used to build a machine learning model to predict their cognitive impairments. RESULTS The classification metrics from the healthy controls were evaluated through the area under the receiver operating characteristic curve and were found to be 0.61, 0.67, and 0.77 for mild cognitive impairment, global cognitive impairment, and mild cognitive impairment with global cognitive impairment, respectively. CONCLUSION Our machine learning model revealed that individuals' acoustic features can be employed to discriminate between healthy controls and those with mild cognitive impairment with global cognitive impairment, which is a more severe form of cognitive impairment compared with mild cognitive impairment or global cognitive impairment alone. It is suggested that language impairment increases in severity with cognitive impairment.

中文翻译：

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通过语音声学分析自动检测认知障碍。

背景技术轻度认知障碍的早期发现对于预防阿尔茨海默氏病至关重要。本研究的目的是确定声学特征是否可以帮助区分患有认知障碍的年龄较大，独立的社区居民和健康对照者。方法共有8779名参与者（平均年龄74.2±5.7，在65-96岁之间，男性3907名，女性4872名），具有不同的认知特征，即健康对照，轻度认知障碍，整体认知障碍（定义为迷你精神状态检查）在短句阅读任务中评估了轻度认知障碍和轻度认知障碍与整体认知障碍（轻度认知障碍和整体认知障碍的合并状态）的得分，以及其听觉特征，包括时间特征（例如发声的持续时间，停顿的次数和长度）和频谱特征（F0，F1和F2），用于建立机器学习模型来预测其认知障碍。结果通过接受者操作特征曲线下的区域评估了健康对照组的分类指标，发现轻度认知障碍，整体认知障碍和轻度认知障碍与整体认知障碍的分别为0.61、0.67和0.77。结论我们的机器学习模型表明，个人的听觉特征可用于区分健康对照和轻度认知障碍伴整体认知障碍的人，与轻度认知障碍或整体认知障碍相比，这是一种更为严重的认知障碍形式。建议语言障碍的严重程度随着认知障碍的增加而增加。