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Currently, spectral analysis occupies an important place among the various methods of investigation of heart rate variability. The result of the spectral analysis is a spectral power density of the study of heart rate variability, which serves as an indicator of the various mechanisms of regulation of the cardiovascular system. A common classical nonparametric method for calculating the spectral power density is the Welch periodogram method, with its underlying fast Fourier transform procedure. This method calculates and then averages a set of spectra obtained from timesequentially shifted segments of the initial dependence of heart rate variability. The resulting dependence of the spectral power density of the signal is a statistical estimate and characterizes the dependence of the distribution of the average power of the signal from the frequency. With this approach, the analyzed dependence of heart rate variability is considered as the realization of some random process. In this case, the amplitude of a peak of the dependence of the spectral power density is not associated with a constant amplitude (or its square) harmonics with a certain frequency and reflects a certain constant rhythm at this frequency, but is associated with the average measure of oscillatory activity (power) analyzed dependence heart rate variability at the frequency under consideration.

The Statistical Accuracy of the Power Spectrum of the Signal of Heart Rate Variability