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集成EMG信号的频率和动态特征作为新的肌肉间协调特征

Shaghayegh Hassanzadeh Khanmiri¹, Peyvand Ghaderyan², Alireza Hashemi Oskouei³

¹Computational NeuroCognitive Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.

Physical and engineering sciences in medicine

|August 21, 2025

概括

这项研究引入了新的动态频率扭曲 (DFW) 功能,使用电肌图 (EMG) 信号准确检测和分类膝盖损伤,提供可靠的自动化系统.

科学领域:

生物医学工程
运动医学
信号处理

背景情况:

膝盖受伤通常涉及肌肉间协调障碍和频率组件的改变.
目前的方法缺乏对这些病理症状的同时量化方法.
需要一个可靠的自动化系统来提高膝关节损伤识别的准确性和一致性.

研究的目的:

开发用于量化膝关节损伤肌肉间协调变化的新特性.
引入动态时间曲线 (DTW) 和动态频率曲线 (DFW) 进行集成的时间频率分析.
使用机器学习分类器评估这些特性的有效性.

主要方法:

使用动态时间扭曲 (DTW) 和动态频率扭曲 (DFW) 来提取特征.
使用支持矢量机 (SVM) 和动态神经网络分类器.
在健康和受伤膝关节的电动肌图 (EMG) 信号的公共数据集上测试了该系统.

主要成果:

动态频率扭曲 (DFW) 功能与级联前进神经网络相结合,在检测方面达到92.03%的准确性,在分类方面达到94.42%.
拟议的特征在四肢间和四肢内部的EMG通道中显示出可靠性.
该系统显示使用更少的EMG通道具有成本效益的膝盖损伤识别潜力.

结论:

开发的DFW功能提供了一种优越的方法来分析膝关节损伤的时间频率特征.
自动化系统提供了一种可靠且一致的方法来识别膝盖受伤.
这项研究通过先进的信号处理为有效和准确的膝盖损伤诊断铺平了道路.

关键词:

前交叉带动态频率变形电磁波信号肌肉之间的协调检测膝盖受伤情况机器学习

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