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The heart rate variability (HRV) is an indicator of the subject"s regulatory system adaptability and it appears the most informative when the subject is under exercise stress which makes his HRV to become a nonstationary process. HRV dynamics provides additional information of individual"s health condition but requires to special approach. The paper contains a research of small-sample statistical method applied to electrocardiogram (ECG) analysis. We present a special method of small-sample imitative addition to increase accuracy of electrocardiogram processing. This ap-proach gives the possibility to observe alteration of RR-intervals standard deviation and probability density distribution as a temporally continuous process. It also lets to evaluate stress index (Baevsky index) time dependence with varying window width. Described procedure of data preprocessing lets to increase accuracy of HRV parameter determination when operated at small data, it"s usage pro-vides the following advantages: feasibility of HRV analyses on any CIG fragment of interest avoiding complicated algorithms of stationary segments researching; capability of use of comparatively short noise free sections when working with noisy and heart rhythm disorderd EEG signals; HRV time history analyzes; feasibility of Baevsky index evaluating during patient’s condition changing; time localization of arrhythmia manifestations. Сonsequently, we elicited practicability of small-sample imitative adding method while HRV analyzing exercise heart rate. A negative attribute of the proposed approach is that absolute measures of HRV as well as absolute value of Baevsky index may not appear to be in agreement with typical values because of different averaging interval. |
