Hari Lakshmi M, G Venugopal, S Ramakrishnan
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:732-735. doi: 10.1109/EMBC44109.2020.9176203.
In this study, an attempt has been made to distinguish between nonfatigue and fatigue conditions in surface Electromyography (sEMG) signal using the time frequency distribution obtained from analytic Bump Continuous Wavelet Transform. For the analysis, sEMG signals from biceps brachii muscle of 22 healthy subjects are acquired during isometric contraction protocol. The signals acquired is preprocessed and partitioned into ten equal segments followed by the decomposition of selected segments using analytic Bump wavelets. Further, Singular Value Decomposition is applied to the time frequency distribution matrix and the maximum singular value and entropy feature for each segment are obtained. The usefulness of both the features is estimated using the Wilcoxon sign rank test that gives higher significance with a p < .00001. It is observed that the proposed method is capable of analyzing the fatigue regions in sEMG signals.
在本研究中，已尝试使用从解析Bump连续小波变换获得的时频分布，来区分表面肌电图（sEMG）信号中的非疲劳和疲劳状态。为了进行分析，在等长收缩方案期间采集了22名健康受试者肱二头肌的sEMG信号。采集到的信号经过预处理并被分成十个相等的段，然后使用解析Bump小波对选定的段进行分解。此外，将奇异值分解应用于时频分布矩阵，并获得每个段的最大奇异值和熵特征。使用Wilcoxon符号秩检验估计这两个特征的有效性，该检验在p < .00001时具有更高的显著性。据观察，所提出的方法能够分析sEMG信号中的疲劳区域。
