桥梁大脑和机器:一种神经认知框架,通过层次语音处理来早期检测阿尔茨海默氏症
IEEE journal of biomedical and health informatics
|December 10, 2025
概括
我们开发了一个新的AI框架,可以在诊断前几年从言语中检测阿尔茨海默病 (AD). 这种神经残留声学学习系统实现了高精度,为早期AD检测提供了一个有前途的工具.
科学领域:
- 计算神经科学是一种神经科学.
- 医疗保健中的人工智能
- 语音信号处理 语音信号处理
背景情况:
- 阿尔茨海默病 (AD) 损害了语言网络,导致早期检测到的语音变化.
- 当前的自动语音分析方法往往忽略了微妙的,诊断上相关的时间语音动态.
研究的目的:
- 引入神经残留声学学习 (NeRAL) 框架,用于使用原始语音波形来早期检测阿尔茨海默病.
- 开发一种以生物听觉处理为灵感的端到端系统,用于增强语音分析.
主要方法:
- 尼拉尔使用声学符号转换,具有扩展卷积的层次时间编码和注意力机制.
- 该系统处理原始音频波形,模仿听觉皮层和前额皮层的功能.
- 在普通话 (NCMMSC2021) 和英语 (ADReSS) 语音数据集上进行评估.
主要成果:
- 在普通话扩展发言中达到86.55%的准确率,在英语演讲中达到70.83%,超过现有方法.
- 在多式联络和大型语言模型方法中表现出卓越的性能.
- 混矩阵分析发现了AD和轻度认知障碍之间的特定错误分类模式,这表明了prodromal声学生物标志物.
结论:
- 通过语音分析,NeRAL框架在早期发现阿尔茨海默病方面取得了重大进展.
- 它的生物灵感建筑增强了捕捉微妙的语音变化.
- 结果支持声学生物标志物的潜力,用于非侵入性,早期的AD查.
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