title: Detection of synchrony in biosignals using cross fuzzy entropy.
abstract: A new method, namely Cross fuzzy entropy (C-FuzzyEn) analysis, that could enable the measurement of the synchrony or similarity of patterns between two distinct signals, was presented in this study. Tests on simulated data sets showed that C-FuzzyEn was superior to the conventional cross sample entropy (C-SampEn) in several aspects, including giving entropy definition in case of small parameters, better relative consistency, and less dependence on record length. The proposed C-FuzzyEn was then applied for the analysis of simultaneously recorded electromyography (EMG) and mechanomyography (MMG) signals during sustained isometric contraction for monitoring local muscle fatigue. The results showed that the C-FuzzyEn of EMG-MMG decreased significantly during the development of muscle fatigue. The time-decrease trend of C-FuzzyEn is similar to the mean frequency (MNF) of EMG, the commonly used muscle fatigue indicator.
authors: Jiangsu University, Jiangsu, 212013 PR of China. xiehb@sjtu.org
authors: Hong-Bo
authors: HB
authors: Xie
authors: Yong-Ping
authors: YP
authors: Zheng
authors: Guo
authors: G
authors: Jing-Yi
mesh_terms: D000328:Adult; D000465:Algorithms; D003198:Computer Simulation; D004576:Electromyography / Q000379:methods*; D019277:Entropy; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007537:Isometric Contraction / Q000502:physiology*; D008297:Male; D009119:Muscle Contraction; D018763:Muscle Fatigue; D016011:Normal Distribution; D013314:Stress, Mechanical; D013997:Time Factors