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当代阿耳茨海默病研究

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ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

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Research Article

通过语音声学分析自动检测认知障碍

卷 17, 期 1, 2020

页: [60 - 68] 页: 9

弟呕挨: 10.2174/1567205017666200213094513

open access plus

摘要

背景：早期发现轻度认知障碍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年龄较大，独立的社区认知障碍者和健康对照者。 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”）在短句阅读任务中评估了考试分数20-23）以及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）及其声音特征，包括时态特征（例如话语的持续时间，停顿的次数和长度）以及频谱特征（F0，F1和F2）被用来建立机器学习模型来预测他们的认知障碍。 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有整体认知障碍的人，这是与轻度认知障碍或整体认知障碍相比更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。

关键词: 轻度认知障碍，整体认知障碍，声学分析，语音，句子阅读，机器学习

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