智能决策系统用于早期发现阿尔茨海默病,使用可穿戴技术和深度学习
R Sathish1, R Muthukumar2, K Manikanda Kumaran3
1Department of Electrical and Electronics Engineering, Kangeyam Institute of Technology, Nathakadaiyur, 638108, Tamil Nadu, India. rsa.researcheee@gmail.com.
Scientific reports
|January 22, 2026
概括
这项研究引入了使用深度学习算法 (ED-DLA) 的早期检测,使用可穿戴传感器早期识别阿尔茨海默病 (AD). 该系统使用深度学习来分析实时数据,改善早期诊断和患者管理.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 目前的阿尔茨海默氏病 (AD) 诊断方法 (临床检查,神经成像) 是侵入性的,昂贵的,并且往往是晚发现疾病.
- 这些传统方法缺乏早期干预所需的实时监测和灵敏度.
- 可穿戴电子设备和深度学习 (DL) 为持续的患者监测提供了一个非侵入性的替代方案.
研究的目的:
- 开发使用深度学习算法 (ED-DLA) 的早期检测,以便及时识别阿尔茨海默病.
- 利用可穿戴传感器数据和DL进行持续的,非侵入性的认知衰退监测.
- 为了提高早期AD检测的准确性,灵敏性和可扩展性.
主要方法:
- 利用可穿戴传感器收集有关心率,睡眠模式和体育活动的实时数据.
- 应用了深度学习算法,特别是基于循环神经网络 (RNN) 的图像分类模型,以分析顺序传感器数据.
- 专注于识别早期阿尔茨海默病的时间模式.
主要成果:
- 该ED-DLA系统在检测阿尔茨海默氏症早期症状方面表现出提高的准确性和灵敏度.
- 通过可穿戴设备进行持续的非侵入性监测,可以识别认知和身体活动中的微妙变化.
- 该RNN模型有效地捕获了对于了解AD进展至关重要的时间数据模式.
结论:
- 拟议的系统提高了早期阿尔茨海默病检测准确度和实时监测能力.
- 可穿戴技术与DL结合,为AD诊断和管理提供了可靠和可扩展的解决方案.
- 通过这种方法早期发现AD有助于及时治疗和干预.
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