ATST-Net:一种用于识别PD上肢和下肢早期症状的方法
Yuanyuan Liu1, Zhaoyi Yang1, Miao Cai2
1School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China.
Medical engineering & physics
|May 24, 2024
概括
这项研究引入了一种新的神经网络方法,用于使用手动特征提取来评估帕金森病 (PD) 勃拉迪基尼西亚. 该方法准确评估多个运动任务,克服现有方法的局限性.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 布拉迪基尼西亚是帕金森病 (PD) 的关键症状,对于早期诊断至关重要.
- 目前用于PD布拉迪基尼西亚的自动评估方法面临设备依赖,任务范围有限和准确性低等挑战.
- 解决这些局限性对于改善早期PD检测和患者管理至关重要.
研究的目的:
- 开发和验证一种统一的,手动的特征提取和基于神经网络的方法,用于评估帕金森病中的布拉迪基尼西亚.
- 克服现有评估方案的局限性,包括小样本规模和单项任务评估.
- 通过使用单一模型,同时评估上肢和下肢运动功能.
主要方法:
- 利用手动特征提取与注意时间系列双流网络 (ATST-Net) 结合进行分类.
- 开发了一个统一的模型,自动评估手指敲击 (FT),手动 (HM),脚敲击 (TT) 和双边脚敏感性 (LA) 任务.
- 收集了120名参与者的数据 (93名PD患者,27名健康对照).
主要成果:
- 获得的分类准确率为FT的0.844,HM的0.819,TT的0.728和LA的0.768.
- 证明了该模型在不同运动任务中的布拉迪基尼西亚分类方面的有效性.
- 统一模型方法在模型培训和转移学习方面显示出显著的优势.
结论:
- 拟议的方法为帕金森病中的布拉迪基尼西亚评估提供了有效的解决方案,解决了当前技术的局限性.
- 本研究提出了第一个统一的模型,用于在PD中同时评估上肢和下肢运动功能.
- 这些发现表明,通过先进的AI驱动的评估工具,可以改善早期PD查和管理.
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