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Pratap Kumar Koppolu¹, Krishnan Chemmangat¹

¹Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology

|November 3, 2023

概括

一种名为Partly EEMD (PEEMD) 的新方法有效地消除了表面肌电图 (sEMG) 信号中的噪声. 这种技术改善了用于康复和假肢等应用的肌肉信号分析.

科学领域:

生物医学工程生物医学工程
信号处理信号处理
康复技术康复技术康复技术

背景情况:

表面肌电图 (sEMG) 信号对于肌肉活动分析至关重要,但易受电力线干扰和运动器件等噪声的影响.
现有的否定方法,如实证模式分解 (EMD) 和其变体 (EEMD,CEEMD),依赖于统计方法来选择内在模式函数 (IMF).

研究的目的:

引入一种新的,自动化的程序来分离噪音IMF和sEMG信号.
通过使用一种新的分解技术来提高sEMG信号的无声化性能.

主要方法:

通过将变 (PE) 集成到 EEMD 选过程中,开发了一种新型的拒绝程序 - - 部分 EEMD (PEEMD).
根据预定义的PE值,PEEMD会自动将噪音较大的IMF分开,重建从所选的IMF发出的无声信号.
该方法在六个上肢运动类别的八名受试者的实验sEMG数据上得到验证,样本度 (SE) 被用作比较措施.

主要成果:

与传统的EMD,EEMD和CEEMD方法相比,PEEMD撤销程序显示出更高的性能.
使用信号与噪声比 (SNR),根平均平方误差 (RMSE) 和重建误差 (RE) 的定量评估证实了PEEMD的有效性.
对所有受试者的平均结果显示,在实验性sEMG数据上的denoising性能显著改善.

结论:

拟议的PEEMD方法提供了一种有效和自动化的方法来拒绝sEMG信号.
这种技术显示出提高基于sEMG的应用在肌肉诊断,康复和假肢方面的准确性和可靠性的巨大潜力.
对于sEMG数据来说,PEEMD的性能优于现有的基于EMD的最先进的denoising技术.

关键词:

拒绝这种行为,就是拒绝.电磁磁指令 (EMD) 是一个电子指令.电动肌图学电动肌图学随机变化的 Entropy 是一个变量.

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