使用不同的矩阵因子化方法来确定中风幸存者的肌肉协同作用
Yehao Ma1, Sijia Ye2, Dazheng Zhao2
1Robotics Institute, Ningbo University of Technology, Ningbo 315211, China.
Medical engineering & physics
|June 18, 2023
概括
与其他方法相比,多变量曲线分辨率交替最小正方形 (MCR-ALS) 显示了肌肉协同分析的优越重复性. 这种算法推用于评估神经系统疾病患者的运动功能.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 康复科学 康复科学 康复科学
背景情况:
- 肌肉协同作用分析对于理解运动控制和评估运动功能至关重要.
- 现有的算法,如非负矩阵分解 (NMF),独立组件分析 (ICA) 和因子分析 (FA),面临着强度的挑战.
- 已经提出了改进的算法,例如单值分解NMF (SVD-NMF),稀疏NMF (S-NMF) 和多变量曲线分辨率-交替最小平方 (MCR-ALS),但直接的性能比较很少.
研究的目的:
- 使用实验电肌图 (EMG) 数据,比较各种肌肉协同识别算法的性能.
- 评估NMF,SVD-NMF,S-NMF,ICA,FA和MCR-ALS的可重复性和人体内一致性.
- 确定分析健康个体和中风幸存者的肌肉协同作用的最合适的算法.
主要方法:
- 从健康个人和中风幸存者中收集了实验电肌图 (EMG) 数据.
- 在EMG数据中应用NMF,SVD-NMF,S-NMF,ICA,FA和MCR-ALS.
- 评估每个算法的可重复性和内主体一致性.
主要成果:
- 与NMF,SVD-NMF,S-NMF,ICA和FA相比,MCR-ALS表现出更高的重复性和人体内一致性.
- 脑卒中幸存者比健康人群表现出更多可识别的肌肉协同作用和较低的体内一致性.
- 这些发现突出了健康和受损运动控制之间的肌肉协同模式的显著差异.
结论:
- MCR-ALS被认为是肌肉协同识别的卓越算法,提供了增强的稳健性和一致性.
- 在中风幸存者中,协同作用的数量增加和一致性减少表明改变了运动控制策略.
- 推MCR-ALS作为一种有利的工具来识别神经系统疾病患者的肌肉协同作用,有助于康复和评估.
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