Method of the study of heart rate variability in children

 

The invention relates to medicine, namely to pediatric cardiology. Check-R-R-interferogram and further spectral analysis carried out on the basis of wavelet transform:, a, bR, a>0, where W(a,b) - coefficient wavelet transform; f(t) is the analyzing function;((t-b)/a) is the analyzing wavelet, then on the basis of wavelet coefficients build scalogram and local spectra. Determine the capacity of the influences on heart rate variability of each of physiologically relevant ranges. Then calculate the time variation of the capacity of the local wavelet spectrum with respect to the previous value, with changes exceeding 15%, evaluate how changes the tone of the studied division of the autonomic nervous system (sympathetic or parasympathetic), which reflects the processes of optimization of heart rate in the changing conditions of the external and internal environment. The invention allows to improve the assessment of autonomic regulation of heart rate variability in children by introducing temporal parameters assess the impact of various departments vegetati which relates to medicine, namely, pediatric cardiology. There is a method of studying the heart rate variability (HRV) is carried out using techniques utilizing modes of temporal and frequency analysis (L. I. Makarov. Holter monitoring. - 2000 - M: Malpractice - S. 51-62). Time analysis is based on calculating the number of statistical parameters of a series of R-R intervals of varying duration (mean, SDNN, SDNN-i, SDANN-i, rMSSD, pNN50, SDSD, Counts) (Crowford MH, Bernstein SJ, Deedwania PC, et al. AHA guidelines for ambulatory electrocardiography: a report of the American College of Cardiology/ American Heart Association Task Force of Practice Guidelines (Commitee to Revise the Guidelines for Ambulatory Electrocardiography). J. Am. Coll Cardiol., 1999; 34:912-48).

This also includes the so-called geometrical methods of HRV analysis - construction of interval histograms, differential histogram differences, correlation ramagrama. Evaluation of the results of geometric methods is carried out by measuring the parameters of the constructed geometric shapes to approximate the pattern of heart rhythm through the construction of figures and mathematical transformation, with subsequent interpretation and direct the description and interpretation of the form of geometric shapes heart rhythm.

General shortcomings of the above methods is the need for long for the characteristics of the local peaks and the absence of a determination of the frequency characteristics of HRV.

When the frequency (spectral) analysis using autoregressive analysis or series of modifications of the fast Fourier transform is the separation of a series of R-R intervals on the frequency spectra of different density. Parameters: spectral power of low frequency (Lf) and low-frequency (VLF) ranges characterizing the influence of the sympathetic and high-frequency (Hf) range, reflecting parasympathetic influence on the regulation of HRV, Lf/Hf ratio, as well as the total power spectrum of fluctuations in R-R intervals (TF).

The main disadvantage of this method is the impossibility of its use in the analysis of non-stationary signals, i.e., most of the patterns of HRV. Fourier's method in the modification of Welch provides averaging of the results of the analysis within the window width, i.e., it is not possible to set the frequency and temporal localization of the fast fading process.

The closest to our proposed method is the window (kratkovremennoe) Fourier transform. But the windowed Fourier transform has the same time resolution and frequency for all points in the plane transformation (N. M.Lidia. Advances in physical Sciences, I. 166, 1996, No. 11, S. 1145-1170, c.l150), which makes this method mathematical expression is positive, and when thin diagnosis of hidden changes of autonomic homeostasis.

The objective of the invention is to improve the detection of changes in the autonomic regulation of cardiac activity in children, which is achieved by analyzing the short-lived patterns of heart rate variability.

The above problem is solved by the fact that the study of heart rate variability includes registration R-R-interferogram and further spectral analysis, according to the invention, is carried out using the continuous wavelet transform by the formula

where W(a,b) - coefficient wavelet transform;

f(t) is the analyzing function (kardiointervalogrammy);

((t-b)/a) is the analyzing wavelet.

Based on the matrix of wavelet coefficients are based scalogram defined as the average of the squares of the wavelet coefficients W(a,b) at a given scale and:

where V(a) - salagrama;

N is the number of wavelet coefficients;

a - scale wavelet transform.

As a function of scale, salagrama reflects the same information as the spectral power density of Fury is no locality properties of wavelets. The wavelet transform, which represents the time base range, allows to obtain more localized in time energy information. Energy diagrams (scalogram) are based on short-term (about 2-3) segments, allowing you to track the temporal dynamics of the process. On salagrama distinguished physiologically relevant frequency ranges that are responsible for different types of regulatory mechanisms.

In Fig.1 shows the algorithm for constructing local wavelet spectrum according to the formula(1), (2), (3), where the upper diagram presents kardiointervalogrammy (on the x-axis is the sequence number of the cardiac cycle; on the y - axis the distance between adjacent cardiac cycles in milliseconds, on average the chart: the x-axis is the sequence number of the cardiac cycle; on the y - axis frequency (scale), grayscale from white correspond to changing the values of the wavelet coefficients (white - 0, black - maximum value of the wavelet coefficients), the lower diagram: the local wavelet spectrum, where the abscissa axis is the frequency (scale) on the y - axis values lnV(a).

In each of the frequency bands was determined by the total value of the wavelet is) U.

where U is the value of the wavelet power density at a certain time interval.

Next, the VPM change in time according to the formula

Thus, evaluation of the dynamics of VPM and evaluation of changes in VPM, i.e., the signal power relative to the previous point in time that allowed us to describe the dynamics of change in the tone of the sympathetic and parasympathetic divisions of the ANS alone for short periods of time.

An example implementation of the algorithm shown in Fig.2, according to the formulas (3) and (4). The local wavelet spectrum of heart rate variability in healthy children (frequency range of the parasympathetic (Hf)). Legend: x-axis ordinal cardiointervals, on the y - axis wavelet power density. Black color represents the values of U (the value of the wavelet power density at each time interval), red - values U(t), i.e. the values changes the tone of the divisions of the ANS in time.

A positive result is associated with improved assessment of autonomic regulation of heart rate variability in children.

Consider the algorithm on the example of SL is p>22 infants born by caesarean section (3rd day of life), see table.1 and 2.

The average number of changes VPM (more precisely, the number of changes in the values VPM previous to subsequent, more than 15%) in the frequency domain of the parasympathetic (HF-band) was 92,30±0,70 changes (hereinafter, the calculation is carried out for N in the formula 3, is equal to 3), for children born by caesarean section, these values were lower and amounted to 89,30±1,10 (p<0.05 with respect to children born through the birth canal). In the frequency domain sympathetic influences (LF band) identified 101,60±0.59 changes for children born by caesarean section, these values were lower and amounted to 102,10±of 0.82 (p>0.05 with respect to children born through the birth canal). In both groups, changes VPM in the frequency range of sympathetic influences was more pronounced (p<0,01) than in the range of the parasympathetic. The result reflects a more active change of sympathetic influences on heart rate variability of the newborn and the suppression of parasympathetic modulation of HRV in children, begotten is latinoa status, identified a subgroup of normotonic (LF/HF=0,99±0,20) and somatotonic (LF/HF=2,37±0,51). Stress adaptation processes and, therefore, sympathicotonia in the early neonatal period in the second subgroup was higher (p<0,05). At the same time, differences on the proposed parameters between subgroups have been identified. This means that the dynamics in time and the sympathetic and parasympathetic influences on HRV in identified subgroups are identical, and the differences concern only the absolute values of U.

Consider now the group of apparently healthy children (61 observation) aged 6 to 12 years of age and 30 children with diabetes mellitus type 1 (DM) with disease duration up to 3 years at the same age.

The results of the spectral analysis by Fourier method are presented in table. 3. Differences between the study groups apply only in the LF range. For children, patients with diabetes, is characterized by a lower tone of the sympathetic division of the ANS. Values parasympathetic influences on HRV and the relative values of the tone of the sympathetic and parasympathetic division of the ANS differences have been identified. When using our proposed algorithm, the differences in the dynamics of VPM for LF-band: in the group of healthy children proposed rate customerquote sympathetic influences on HRV in children patients with diabetes. The analysis of the dynamics of parasympathetic modulation of HRV was 55,76±5,16 changes for the control group and 78,03±1.57 changes for a group of children, patients with DM. These changes also reflect a higher activity of the parasympathetic influences on HRV in the initial stages of the disease.

The revealed changes reflect a more dynamic effect of the ANS on the heart rate variability of sick children.

Consider now the application of the algorithm on the example of individual children.

CLINICAL EXAMPLES

As an example, we used the results of the registration and processing of HRV have two children somatotonic. As an example of somatotonic were chosen not by chance that these children for short periods of time (1-3 min) HRV may be relatively long to maintain a low amplitude of all their temporal components, which is difficult for spectral analysis. Duration of registration was 300-R-interval. Averaging was performed over 3-m R-interval.

Ave.1. Child, P., 12 years: according to time HRV analysis: the average heart rate of 105 beats/min, mean - 0,57 with, variance - 0,005, standard deviation deletelinks of tension of regulatory systems - 73, index of autonomic balance - 83,9, asymmetry of 0.01, the kurtosis is 3.23, the index of centralization - 4,06. According to frequency analysis using Fourier transform: LF=1187 Hz2, HF=287 Hz2, VLF=1547 Hz2.

According to the results of wavelet analysis: the total duration of non-stationary fragments 158 cardiocycle, the total capacity of non-stationary fragments is 879654.E., the distribution of values of the 1st derivative of the normal (skewness - 0.03, kurtosis - 3,1, mathematical expectation - 253.E., the standard deviation 48.E.). The number of changes VPM (more precisely, the number of changes in the values VPM previous to subsequent, more than 15%) in the frequency domain of the parasympathetic (HF-band) was 28 changes in the frequency domain sympathetic influences (LF band) was 56 changes.

Ave.2. Child F., age 12, according to the time HRV analysis: the average heart rate is 110 beats/min, mean - 0,54 with, the variance of 0.002, the standard deviation of the durations of R-R interval - 0.04, fashion - 0,52 with, the mode amplitude is 50%, the variation range of 0.66, the coefficient of variation - 7,24, index of tension of regulatory systems is 69.6, index of autonomic balance - 75,7, asymmetry of 0.01, the kurtosis is 4.6, the index of the centre is LF=1734 Hz2.

According to the results of wavelet analysis: the total duration of non-stationary fragments is 231 cardiocycle, the total capacity of non-stationary fragments equal 683129.E., the distribution of values of the 1st derivative -(the asymmetry is 1.5, kurtosis - 4,2, mathematical expectation - 115.E., the standard deviation 69.E.). The number of changes VPM (more precisely, the number of changes in the values VPM previous to subsequent, more than 15%) in the frequency domain of the parasympathetic (HF-band) was 68 changes in the frequency domain sympathetic influences (LF band) was 87 changes.

As can be seen from these examples, when relatively similar spectral and statistical indices of heart rate variability for each investigated child, temporal characteristics, obtained using the proposed algorithm processing cardiointervalogram based on wavelet analysis, have significant differences. This allows you to talk about the different types of temporal dynamics of vegetative regulation changes in heart rate children in time and, consequently, about the various mechanisms that maintain vegetative homeostasis in children with the th regulation of heart rate variability in children.

Claims

Method of the study of heart rate variability child, including check-R-R-interferogram and further spectral analysis, wherein the spectral analysis is carried out using the continuous wavelet transform by the formula

a, bR, a>0,

where W(a,b) - coefficient wavelet transform;

f(t) is the analyzing function,

((t-b)/a) is the analyzing wavelet;

next, on the basis of wavelet coefficients build scalogram by formulas

where V(a) - salagrama;

N is the number of wavelet coefficients;

a - frequency wavelet transform;

where U is the value of the wavelet power density, reflecting the capacity of the local wavelet spectrum in each of physiologically relevant ranges,

U(t) = U(t1)-U(t2)

where U(t) reflects the change in time of the power of the local wavelet spectrum with respect to the previous value, with changes exceeding 15%, evaluate how changes the tone of the studied Department in the

 

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FIELD: medicine.

SUBSTANCE: method involves recording heart beat rate and systolic arterial blood pressure before and after two-stage exercise stress. The first stage is of 50 W within 3 min and the second one is of 75 W during 2 min. Patient rest pause is available between loading stages to recover initial heart beat rate. Prognostic estimation of cardiopulmonary complications is carried out with mathematical formula applied.

EFFECT: reduced risk of complications in performing tests.

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