The application of the Hilbert spectrum to the analysis of electromyographic signals
Information Sciences: an International Journal
Computer Methods and Programs in Biomedicine
Cross-fuzzy entropy: A new method to test pattern synchrony of bivariate time series
Information Sciences: an International Journal
Complexity analysis of the biomedical signal using fuzzy entropy measurement
Applied Soft Computing
Computers in Biology and Medicine
Journal of Intelligent and Robotic Systems
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The mean frequency (MNF) of surface electromyography (EMG) signal is an important index of local muscle fatigue. The purpose of this study is to improve the mean frequency (MNF) estimation. Three methods to estimate the MNF of non-stationary EMG are compared. A novel approach based on Hilbert-Huang transform (HHT), which comprises the empirical mode decomposition (EMD) and Hilbert transform, is proposed to estimate the mean frequency of non-stationary signal. The performance of this method is compared with the two existing methods, i.e. autoregressive (AR) spectrum estimation and wavelet transform method. It is observed that our method shows low variability in terms of robustness to the length of the analysis window. The time-varying characteristic of the proposed approach also enables us to accommodate other non-stationary biomedical data analysis.