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The empirical mode decomposition (EMD) combines with Hilbert Transform is a common method of nonlinear and non-stationary signal time frequency analysis. Signal can be decomposed into different intrinsic mode functions (IMF) through the EMD. Each IMF represents a simple oscillation which provides meaningful instantaneous frequency through Hilbert transform. The mode mixing of IMF is a critical problem...
Frequency estimation in passive location based on non-cooperative information sources plays an important role. And existing methods such as wavelet transform (WT) can't satisfy the precision of frequency estimation what is needed in location. In order to solve the puzzle, the Hilbert-Huang transform(HHT) is adopted in this article. HHT is based on the local characteristics of signals, which is an...
Aimed at the nonlinear and non-stationary signals, a method of time-frequency analysis which is called HHT based on Empirical Mode Decomposition is introduced. This method makes any complicated set of data to be decomposed into a finite and often small number of Intrinsic Mode Functions through Empirical Mode Decomposition. So the instantaneous frequency defined by Hilbert transform has a meaning...
Spectral analysis of heart rate variability (HRV) is used for the assessment of cardiovascular autonomic control. In this study data driven adaptive technique empirical mode decomposition (EMD) and the associated Hilbert spectrum has been used to evaluate the effect of local anesthesia on HRV parameters in a group of fourteen patients undergoing brachial plexus block (local anesthesia) using transarterial...
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