生物标志物 生物标志物
Liangwei Tao1, Zhixing Zhou2, Huanhuan Xia2
1University of Shanghai for Science and Technology, Shanghai, China.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 24, 2025
概括
一种新的基于语音的方法,使用Pinyin序列和声学特征,改善了在中文中检测阿尔茨海默病的情况. 这种方法为早期认知障碍查提供了一个非侵入性和高效的工具.
科学领域:
- 计算语言学计算语言学
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
背景情况:
- 阿尔茨海默病 (AD) 诊断依赖于侵入性和昂贵的方法,限制了早期检测.
- 语音分析为认知障碍检测提供了一个非侵入性的替代方案.
- 中国的同音声带来了转录的挑战,可能会降低基于语音的模型准确性.
研究的目的:
- 开发和验证一种新的基于语音的方法,以改善在讲汉语的人群中检测阿尔茨海默病.
- 通过使用 Pinyin 序列来克服同音声引起的转录模两可.
- 提高早期认知障碍查的效率和准确性.
主要方法:
- 利用来自中国临床前阿尔茨海默病研究 (C-PAS) 队列300名参与者的音频数据.
- 将语音转录为 Pinyin 序列并提取声学特征 (例如,暂停频率,MFCC).
- 采用双向LSTM模型来整合Pinyin序列,声学特征和人口统计数据,并结合数据增强技术.
主要成果:
- 提出的方法实现了93.80%的准确性和0.93.9的AUC.
- 整合Pinyin序列与声学特征显著优于仅使用声学数据或认知分数的模型.
- 废弃研究证实了结合语言 (Pinyin) 和声学特征的重要性.
结论:
- 整合Pinyin序列和声学特征是一种可行且有效的策略,用于在中文中非侵入性地检测阿尔茨海默氏症.
- 这种方法为早期阿尔茨海默氏症查提供了一个实用的工具.
- 结果支持进一步的大规模研究和潜在的临床应用用于认知障碍检测.
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