使用集体实证模式分解和卷积式双向LSTM自动区分自愿和动运动
Jie Qi Ling1, A S M Bakibillah2,3, Ping Yi Chan4,5,6
1School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500, Selangor, Malaysia. jie.ling@monash.edu.
Scientific reports
|October 8, 2025
概括
这项研究引入了一种新的AI方法,结合了EEMD和LSTM,以准确地区分帕金森发和患者的自愿运动. 该方法实现了高精度,有助于开发更好的帕金森病辅助应用程序.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 神经信号处理神经信号处理
背景情况:
- 准确地提取帕金森震对于开发帕金森病患者的辅助技术至关重要.
- 传统的选方法具有局限性,包括需要预设的频率范围和可能包含不需要的信号.
- 现有的AI应用专注于疾病分类,不区分PD患者的动和自愿动作.
研究的目的:
- 开发一种自动化的方法来区分PD患者的自愿和动运动.
- 克服传统过技术的局限性,用数据驱动的分解方法取代它们.
- 通过准确识别帕金森发作,使得PD的高级辅助应用程序的开发成为可能.
主要方法:
- 使用集体实证模式分解 (EEMD) 来将PD患者的原始手臂方向数据分解为子信号,取代了传统的过.
- 在双向长短期存储器 (LSTM) 网络中使用一个卷积层,自动从EEMD子信号中提取特征.
- 使用提取的特征训练了一个深度学习分类器,消除了手动特征工程的需要.
主要成果:
- 拟议的EEMD组合卷积双向LSTM方法在区分自愿和动动作时实现了94.2%±1.1%的准确性.
- 该方法显示了较低的平方根平均误差,表明可以从原始信号中准确地提取帕金森震.
- 这种方法被证明可以用于识别在日常活动中没有预先识别的特征的运动信号的震动.
结论:
- 开发的AI方法有效地区分了帕金森发与使用EMD和LSTM的自愿运动.
- 这种方法为震动提取提供了一个强大的,自动化的解决方案,适用于现实世界的活动.
- 这些发现为改善帕金森病患者的辅助技术和个性化干预措施铺平了道路.
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