从非语义,声学语音特征检测轻度认知障碍:弗雷明汉心脏研究
Huitong Ding1,2, Adrian Lister3, Cody Karjadi1,2
1Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States.
JMIR aging
|August 22, 2024
概括
语音分析可以早期检测轻度认知障碍 (MCI). 机器学习识别了语音录制中的关键声学特征,如填充的暂停,用于准确检测MCI,有助于早期阿尔茨海默病和相关痴呆症 (ADRD) 的评估.
科学领域:
- 神经学 神经学
- 语音科学 语言科学
- 机器学习 机器学习
背景情况:
- 人口老龄化正面临着阿尔茨海默病和相关痴呆症 (ADRD) 的日益增加的负担.
- 早期识别轻度认知障碍 (MCI) 对于及时干预至关重要,以潜在地减缓痴呆症的进展.
- 语音分析为认知评估提供了一种具有成本效益的,非侵入性的方法,这是由于语音生产所涉及的复杂认知过程.
研究的目的:
- 开发一种机器学习管道,用于识别MCI的语音录音中的声学特征.
- 评估这些声学特征对于检测MCI的能力.
主要方法:
- 构建了一个机器学习管道,包括扬声器日记化,特征提取,特征选择和分类.
- 分析了来自弗雷明翰心脏研究的100例MCI病例和100例对照.
- 从语音录音中提取了6385个声学特征,并使用一个随机森林模型进行分类.
主要成果:
- 29个特征的最佳子集实现了接收器操作特征曲线下的面积为0.87 (95% CI:0.81-0.94).
- 填充停顿的数量是MCI分类中最重要的声学特征.
- 模型的性能在不同的语音录音长度中是一致的.
结论:
- 语音分析,专注于非语义和声学特征,显示了早期ADRD检测的希望.
- 语音录音可以作为监测大脑健康的宝贵工具.
- 这种方法支持未来的研究,利用语言来早期检测认知衰退.
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