most promising method of time-frequency analysis of the data is the
Hilbert-Huang Transform, which makes it possible to work with nonstationary and nonlinear data. The method is based on the
Empirical Mode Decomposition of signals and the Hilbert Transform. The key feature of Empirical Mode Decomposition is
to decompose a signal into so-called Intrinsic Mode Function (IMF). IMF
represents a simple oscillatory mode as a counterpart to the simple harmonic
function, but it is much more general: instead of constant amplitude and
frequency in a simple harmonic component, an IMF can have variable amplitude
and frequency along the time axis. Further-more, the Hilbert Spectral Analysis
of Intrinsic Mode Functions provides frequency information evolving with time
and quantifies the amount of variation due to oscillation at different time
scales and time locations. The paper shows
problems solving of applying Hilbert-Huang Transform for biomedical signal
processing. It is the presence of oscillations of very disparate amplitude in a
mode, or the presence of very similar oscillations in different modes. Complete
Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) can effectively
overcome these problems, and potentially should provide more objective results
than alternative methods. In the CEEMDAN method a particular noise is added at
each stage of the decomposition and a unique residue is computed to obtain each
pulse signal, Hilbert-Huang transform, Complete Ensemble Empirical Mode
Decomposition with Adaptive Noise.
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