|本期目录/Table of Contents|

[1]宋方禹△,刘烨辉,朱立华,等.基于小波变换的肌肉疲劳表面肌电信号特征提取的研究*[J].生物医学工程研究,2019,01:86-89.
 SONG Fangyu,LIU Yehui,ZHU Lihua,et al.Feature extraction of muscle fatigue surface electromyogram signal based on wavelet transform[J].Journal of Biomedical Engineering Research,2019,01:86-89.
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基于小波变换的肌肉疲劳表面肌电信号特征提取的研究*(PDF)

《生物医学工程研究》[ISSN:1006-6977/CN:61-1281/TN]

期数:
2019年01期
页码:
86-89
栏目:
出版日期:
2019-03-25

文章信息/Info

Title:
Feature extraction of muscle fatigue surface electromyogram signal based on wavelet transform
文章编号:
1672-6278 (2019)01-0086-04
作者:
宋方禹1△刘烨辉1朱立华1朱峻岭1亓勤德1朱江2
1.山东济南市人民医院神经内科,莱芜 271100;2.湖南科技大学机电工程学院,长沙 410000
Author(s):
SONG Fangyu1LIU Yehui1ZHU Lihua1ZHU Junling1QI Qinde1ZHU Jiang2
1.Department of Neurology,Jinan People′s Hospital,Laiwu 271100,China; 2.Hunan University of Science and Technology, School of Mechanical Engineering,Changsha 410000,China
关键词:
小波变换时域特征频域特征表面肌电信号肌肉疲劳
Keywords:
Wavelet transform Time domain feature Frequency domain feature Surface electromyography Muscle fatigue
分类号:
R318.04
DOI:
10.19529/j.cnki.1672-6278.2019.01.18
文献标识码:
A
摘要:
当前肌肉疲劳表面肌电信号(surface electromgography,sEMG)特征提取方法,忽略了非线性跳错信号的影响,且不能在非平稳状态下进行特征提取,存在特征提取准确度差的问题。提出基于小波变换的肌肉疲劳sEMG特征提取研究,采用小波变换对所采集的样本去噪,结合时域、频域特征分析法,融合傅里叶变换方法对肌电信号中的线性特征进行提取,根据带谱近似熵理论对非线性挑错信号进行特征回归分析,并利用拟态分解函数和希尔伯特变换法对肌电信号进行时频特征的整合提取,最终完成基于小波变换的肌肉疲劳sEMG特征提取研究。实验验证,所提方法具有可行性,且将1000个肌电信号样本分成5组,对其中的跳错信号进行特征提取,所提方法准确度较文献方法高出75%,在非平稳状态下将200个肌电信号样本分成5组进行特征提取,所提方法准确度较文献方法高出33%。由此得出,所提方法优于当前特征提取方法。
Abstract:
At present, the feature extraction method of muscle fatigue surface electromyogram (MSEMG) signal ignores the influence of non-linear error-jumping signal, and can not extract feature in non-stationary state, which causes poor accuracy of feature extraction.To propose the feature extraction of muscle fatigue surface electromyogram signal based on wavelet transform. Wavelet transform was used to denoise the collected samples. The linear feature of EMG signal was extracted by combining time-domain and frequency-domain feature analysis with Fourier transform method. According to band-spectrum approximate entropy theory, the feature regression analysis of non-linear fault-detection signal was carried out, and the pseudo-decomposition function and Hirsch function were used. The time-frequency features of EMG signals were integrated and extracted by the method of Albert transform. Finally, the feature extraction of muscle fatigue surface EMG signals based on wavelet transform was completed. Experiments showed that the proposed method was feasible, and 1 000 EMG samples were divided into five groups to extract the feature of the error signal. The accuracy of the proposed method was 75% higher than that of the literature method. In the non-stationary state, 200 EMG samples were divided into five groups to extract the feature. The accuracy of the proposed method was 33% higher than that of the literature method. It is concluded that the proposed method is superior to the current feature extraction method.〖WTHZ〗Key words:〖WTBZ〗Wavelet transform; Time domain feature; Frequency domain feature; Surface electromyography; Muscle fatigue

参考文献/References

备注/Memo

备注/Memo:
(收稿日期:2018-10-25)山东省卫计委项目(2015PYA021)。△通信作者Email:SFYCCL@163.com
更新日期/Last Update: 2019-05-26