语音数据的深度学习用于早期发现老年人的阿尔茨海默病
Kichan Ahn1, Minwoo Cho2,3,4, Suk Wha Kim3,5
1Interdisciplinary Program in Medical Informatics Major, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
Bioengineering (Basel, Switzerland)
|September 28, 2023
概括
这项研究使用语言分析来检测阿尔茨海默病 (AD),通过Densenet121模型实现高精度. 通过可访问的远程医疗保健查,改善了早期AD检测.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 阿尔茨海默氏症 (AD) 是一种普遍的痴呆症形式,导致患者和家庭遭受重大痛苦.
- 早期发现AD对于及时干预和症状管理至关重要.
研究的目的:
- 通过分析语音和语言特征,开发一种快速AD检测方法.
- 为了利用深度学习来识别阿尔茨海默病的语言标志物.
主要方法:
- 利用了来自韩国公共卫生中心的迷你精神状态检查痴呆查 (MMSE-DS) 问卷数据.
- 将选定的语音数据转换为基于Mel频率切布斯特拉系数 (MFCC) 的光谱图像.
- 使用Densenet121进行数据增强,并训练了五个深度学习模型 (Inception v3,VGG19,Xception,Resnet50,Densenet121)进行分类.
主要成果:
- 五倍交叉验证比保留方法产生了更显著的结果.
- Densenet121模型实现了0.9550的灵敏度,0.8333的特异性和0.9000的准确性,在区分阿尔茨海默病患者和对照组中.
- 该模型在基于语言特征的阿尔茨海默病分类方面表现出强的表现.
结论:
- 拟议的方法简化了阿尔茨海默病的查,提高了远程医疗保健能力.
- 增加对AD查的可访问性可以导致更高的早期诊断和干预率.
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