Heart Rate Variability: for real doctors. About transients and a special place of bio-adaptive control. Transient processes

Yabluchansky N.I., Martynenko A.V.

Heart rate variability: for real doctors. Translation from the Russian version of the book, published at Kharkiv, 2010, 131 p.
The basics and practice of the clinical use of the technology of heart rate variability are outlined for doctors of all specialties and students of medical faculties of universities.

10. About transients and a special place of bio-adaptive control

Transient processes

In stationary conditions, the regulation behaves in a stationary manner, and, consequently, all functions controlled by it behave in the same way. HRV is no exception.
Standard HRV technologies “built” on spectral analysis, to which all previous chapters of the book are devoted, are based on processing stationary sections of the rhythmogram, which is provided by standard conditions of the patient’s status for the period of rhythmogram registration in one or another fixed body positions, or at cyclical kinds of stress. At the same time, the rhythmogram sections corresponding to transitional processes are deliberately excluded from the spectral decomposition.
Life, however, is a continuous transition. Moreover, it is well known that earlier violations occur and, therefore, manifest themselves, namely in transients. Consequently, it is natural to strive to expose to the study of HRV in transients, too.
The standard technologies of HRV are not suitable for the study of transients, and unique methods are developed here.
Mathematics defines stationarity as a property of a probabilistic process to remain constant over time. In this case, two aspects are usually considered: more strict, when the process is invariant concerning the time shift, and simplified, when only its expectation (average value) does not depend on time, in our case, the length of the cardiac intervals.
For a rigorous assessment of the stationarity of the cardiac intervals, we have developed the so-called M-indices, which allow us to accurately assess how much the series resembles itself during the observation period, thus highlighting areas of “self-similarity” (stationarity). The standard and outlined HRV methods are applied to these series. As for the series, nonstationarity, we have proposed methods for its qualitative assessment — increasing or decreasing the nonstationarity degree and the degree of its difference from linear variation. You can find more about these methods in the Appendix.

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