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一个新的分数模糊分散及其在肌肉疲劳检测中的应用

Baohua Hu¹, Yong Wang², Jingsong Mu^2,3

¹School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China.

Mathematical biosciences and engineering : MBE

|February 2, 2024

概括

分数模糊分散 (FFDispEn) 在非线性动态中增强动态变化灵敏度. 与其他方法相比,FFDispEn在sEMG信号中显示出优异的肌肉疲劳检测.

科学领域:

非线性动力学是一种非线性动力学.
生物医学信号处理
复杂性分析是一种复杂性分析.

背景情况:

模糊分散 (DispEn) 是一种非线性动力学方法,结合了DispEn和模糊.
DispEn对动态变化的灵敏度有局限性.
表面电肌图 (sEMG) 信号对于肌肉疲劳分析至关重要.

研究的目的:

提出分数模糊分散 (FFDispEn) 以提高对动态变化的灵敏度.
评估FFDispEn在通过sEMG信号检测肌肉疲劳方面的有效性.
为了比较FFDispEn与样本 (SampEn) 和DispEn.

主要方法:

FFDispEn是使用一种新的模糊成员函数和分数运算来开发的.
分析了来自1D物流图的模拟信号和来自29名受试者的sEMG信号.
使用滑窗方法计算信号复杂性,使用SampEn,DispEn和FFDispEn.

主要成果:

与DispEn相比,FFDispEn对动态变化的灵敏度更高,灵敏度随分数次序q的增加而增加.
在肌肉疲劳期间,FFDispEn表现出明显的下降趋势,平均斜率为-1.658 × 10−3,表现优于SampEn和DispEn.
与SVM结合的FFDispEn在检测肌肉疲劳时获得了97.5%的准确性.

结论:

FFDispEn是一种敏感的非线性动态指标,用于sEMG信号处理和肌肉疲劳分析.
拟议的FFDispEn方法比现有的生理和生物医学信号分析方法提供了更好的性能.
FFDispEn具有在各种生理和生物医学信号分析任务中的应用潜力.

关键词:

复杂性分析分析复杂性分析分数微积分的微积分计算.分数模糊分散的.肌肉疲劳肌肉疲劳肌肉疲劳这是一个 sEMG 信号.

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