在多个语音和语言领域对认知衰退和可能的阿尔茨海默氏症痴呆的自动分类
Rui He1, Kayla Chapin1, Jalal Al-Tamimi2
1Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
American journal of speech-language pathology
|July 24, 2023
概括
机器学习使用语音模式准确地检测早期阿尔茨海默病 (AD) 和认知衰退. 语音特征,特别是语音质量,在识别轻度认知障碍 (MCI) 等情况中的微妙语言变化方面优于文本分析.
科学领域:
- 神经语言学是一种神经语言学.
- 计算语言学计算语言学
- 发现生物标志物的发现.
背景情况:
- 语言衰退是阿尔茨海默病 (AD) 的潜在早期生物标志物.
- 目前对早期AD阶段 (SCD,MCI) 跨任务,领域和语言的语言测量灵敏度的理解有限.
- 在早期认知衰退中可靠检测微妙的语言变化需要进一步调查.
研究的目的:
- 评估各种语言措施的敏感性,以检测认知能力下降.
- 评估语音和文字特征在识别可能患有AD痴呆症,MCI和SCD的个体中的可区分性.
- 在机器学习分类中比较不同语言领域的性能.
主要方法:
- 在西班牙语/加泰罗尼亚语队列 (N=119) 中使用场景构建任务来引起语音.
- 提取了声学 (光谱,声谱,语音质量),表音和文本 (形态,语义,语法) 的特征.
- 采用随机森林分类器来区分可能的AD痴呆症,遗忘/非遗忘MCI,SCD和健康对照.
主要成果:
- 机器学习分类器实现了高性能得分 (>.9).
- 语音特征显著优于文字特征,语音质量是最有效的.
- 即使在健康对照和SCD之间,以及在MCI子组内,也观察到高诊断准确度.
结论:
- 基于语音的机器学习有效地检测认知衰退和可能的AD痴呆症.
- 在各种语言和语音特征领域的诊断能力存在显著差异.
- 语音质量分析显示,对早期AD检测特别有希望.
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