利用声音的力量:用于阿尔茨海默病检测的深度神经网络模型
Chan-Young Park1, Minsoo Kim2, YongSoo Shim3
1Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea.
Dementia and neurocognitive disorders
|February 16, 2024
概括
语音分析显示了检测认知障碍和阿尔茨海默病的潜力. 虽然还不是临床工具,但这种方法为早期诊断和改善患者结果提供了一个有希望的途径.
科学领域:
- 神经科学是一个神经科学.
- 语音和听力科学 语言和听力科学
- 人工智能的人工智能
背景情况:
- 语音特征可以反映潜在的大脑功能.
- 认知障碍 (CI) 和阿尔茨海默病 (AD) 影响认知能力,可能会改变语言模式.
- 早期发现CI和AD对于及时干预和管理至关重要.
研究的目的:
- 调查语音数据在区分正常认知,轻度认知障碍 (CI) 和阿尔茨海默病痴呆症 (ADD) 之间的实用性.
- 评估深度神经网络 (DNN) 模型在基于语音特征的认知状态分类方面的表现.
主要方法:
- 在三个组中招募了参与者:主观认知衰退,轻度CI和ADD.
- 使用Mel频率的塞普斯特拉系数和Chroma.提取的语音特征.
- 采用深度神经网络 (DNN) 模型进行分类.
主要成果:
- 在最初的试验中,DNN模型在预测ADD时达到约81%的准确性.
- 在未见的数据集上测试时,性能平均提高到82.0%±1.6%.
- 结果表明,在认知评估中,语音分析是一个有希望的概念验证.
结论:
- 使用DNN进行的语音分析显示了早期检测AD的非侵入性方法的潜力.
- 虽然目前的准确性水平需要进一步改进以获得临床应用,但这些发现支持继续研究.
- 这种方法可以提高诊断准确性和可访问性,可能改善患者的治疗结果.
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