基于波形和频谱语音信号处理的阿尔茨海默病的识别
Ying Gu1, Jie Ying1, Quan Chen1
1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093 China.
Biomedical engineering letters
|January 9, 2025
概括
语音分析提供了一种新方法来检测阿尔茨海默病 (AD). 一个新的AD识别网络 (ADNet) 分析了语音特征,显示了早期AD检测的前景,并将其与正常认知区分开来.
科学领域:
- 神经科学是一个神经科学.
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病.
- 目前的AD诊断依赖于侵入性和昂贵的方法.
- 早期发现AD对于有效管理至关重要.
研究的目的:
- 提出一个新的多道网络框架 (ADNet) 用于使用语音分析识别阿尔茨海默病.
- 为了研究将时间域和频域语音特征集成为AD检测的有效性.
主要方法:
- 开发了ADNet,这是一个多通道网络,集成了时域和频域语音功能.
- 使用波形图像和log-Mel光谱图作为语音信号数据源.
- 员工倒置剩余块和封闭的多信息单元,用于特征学习和整合.
主要成果:
- 拟议的ADNet在上海认知查 (SCS) 数据集上取得了高性能.
- 实现了准确率为88.57%,精度为88.67%,回忆率为88.64%.
- 超越了现有的基于语音的阿尔茨海默病识别方法.
结论:
- 该ADNet框架有效地区分了阿尔茨海默病患者与健康对照.
- 语音分析,特别是使用拟议的ADNet,显示了早期AD识别工具的巨大潜力.
- 这种方法为AD查提供了一种非侵入性和潜在的成本效益高的方法.
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