**Abstract. **The
paper presents the results of the using of Hilbert-Huang Transform as an
instrument for digital processing biomedical signals. It is possible to obtain
information on the characteristics of the time-varying process in conditions of
noises and nonstationary dynamics. Hilbert-Huang Transform is a method of
time-frequency analysis of signals. It is possible to evaluate instantaneous
values of the frequencies and their amplitudes at every instant. This method is
designed to use the Hilbert Transform for single-frequency functions that are
obtained in the process of empirical mode decomposition.

It has been shown that about 8 – 11
mode functions are distinguished in the process of empirical mode decomposition
of blood pressure signals. The number of mode functions is depending on the
structure of the signal and the presence of noises. It has been revealed that
over 95% of the signal power is contained in 4th-6th mode functions (*imf*_{4}-imf_{6}).
The functions *imf*_{1}**-***imf*_{3}
are high frequency noises; the functions *imf*_{7}**-***imf*_{9}
are low frequency trend.

The conclusion has been made that
this approach makes it possible to identify a number of rhythmic processes in
the structure of a valid arterial blood pressure signal. For the estimation of
the time-frequency characteristics significance, the survey sample consists of
3 groups. It has been shown that the dynamics of mode functions differs in all
groups. For the quantitative estimation of the time-frequency analysis data,
for each Hilbert spectrum of the mode functions, weighted average frequency and
weighted square deviation were defined. Taking into account the complicated
character of the registered signals, the use of the high-accuracy methods of
examining the nonstationary noisy signals like as Hilbert-Huang Transform
permits revealing the characteristic features of the arterial blood pressure
signals that improve the diagnostication quality.

**Keywords:**
Hilbert-Huang transform, time-frequency analysis, pulse signal.

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