scholarly journals Harmonic signal extraction from chaotic interference based on synchrosqueezed wavelet transform

2015 ◽  
Vol 64 (10) ◽  
pp. 100201
Author(s):  
Wang Xiang-Li ◽  
Wang Bin ◽  
Wang Wen-Bo ◽  
Yu Min ◽  
Wang Zhen ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Harmonic Signal ◽  
Signal Extraction ◽  
Synchrosqueezed Wavelet Transform ◽  
Chaotic Interference

scholarly journals HARMONIC SIGNAL EXTRACTION FROM STRONG CHAOTIC INTERFERENCE

2001 ◽  
Vol 50 (6) ◽  
pp. 1019
Author(s):  
WANG FU-PING ◽  
GUO JING-BO ◽  
WANG ZAN-JI ◽  
XIAO DA-CHUAN ◽  
LI MAO-TANG
Keyword(s):  
Harmonic Signal ◽  
Signal Extraction ◽  
Chaotic Interference

scholarly journals Sleep Spindle Detection Using RUSBoost and Synchrosqueezed Wavelet Transform

2020 ◽  
Vol 28 (2) ◽  
pp. 390-398 ◽  
Author(s):  
Takafumi Kinoshita ◽  
Koichi Fujiwara ◽  
Manabu Kano ◽  
Keiko Ogawa ◽  
Yukiyoshi Sumi ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Sleep Spindle ◽  
Synchrosqueezed Wavelet Transform

Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features

2021 ◽  
pp. 095441192110480
Author(s):  
Lakshmi M Hari ◽  
Gopinath Venugopal ◽  
Swaminathan Ramakrishnan
Keyword(s):  
Wavelet Transform ◽  
Biceps Brachii ◽  
Singular Value ◽  
Time Frequency ◽  
Singular Value Decomposition Method ◽  
Dynamic Contractions ◽  
Synchrosqueezed Wavelet Transform ◽  
Value Decomposition ◽  
Neuromuscular Conditions ◽  
Semg Signals

In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

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