A New Tool for Nonlinear Dynamical Analysis of Heart Rate Variability

1998 ◽  
Vol 53 (3-4) ◽  
pp. 112-116 ◽  
Author(s):  
A. Posiewnik ◽  
J. Da̢bkowski

Abstract In this paper we analyse the sequences of the time intervals between heart-beats-the RR intervals-by means of AIP (artificial insymmetration patterns) diagrams.The sequences were produced by artificial heartbeat sequences generated numerically and compared with sequences obtained from real heart activity.We hope that the AIP diagrams method will prove useful for a rapid qualitative assessment of dynamics from nonlinear time series, and that it is able to distinguish various types of heart dynamics (regular and pathological), while other diagnostical methods fail.

Fractals ◽  
2021 ◽  
pp. 2150201
Author(s):  
HAMIDREZA NAMAZI

Analysis of the variations of heart activity during different human activities is an important area of research in sport sciences. Therefore, in this paper, we evaluated the variations of heart activity for 23 subjects while sitting, hand biking, walking, and running. Since the obtained R-R time series (as the indicator of heart rate variability (HRV)) has a complex structure that contains information, we employed fractal dimension, sample entropy, and Shannon entropy for our analysis. According to the results, doing a harder activity causes a more significant alteration in the complexity and information content of HRV. The results of statistical analyses also verified the obtained results. Similar investigations can be conducted in case of other activities to evaluate the variations in heart activity in different conditions.


2020 ◽  
Vol 5 (6) ◽  
pp. 342-348
Author(s):  
L. S. Vovkanych ◽  
◽  
V. M. Sokolovskii ◽  
Y. R. Boretskii ◽  
D. I. Berhtraum ◽  
...  

The important task for modern physiology is remote monitoring of the functions of physiological systems of the human organism during the competitive and training activity. It is well known that analysis of heart rate variability is one of the effective methods to evaluate the physiological changes which occur in the response to physical loads. In order to perform the correct analysis of heart rate variability by newly designed devices, it is necessary to confirm the sufficient level of accuracy in the registration of RR intervals. The purpose of our research was to analyze the accuracy of RR time series measurements by software-hardware complex “Rytm” and validity of subsequently calculated heart rate variability indexes in conditions of exercise performance. Material and methods. The study involved 20 healthy male adults 20-21 years old. Recording of cardio intervals was performed simultaneously with «Polar RS800», and software-hardware complex “Rytm”. The subjects performed a step test in a rate of 20 steps per minute, platform height – 40 cm, duration – 2 minutes. Results and discussion. Heart rate variability indexes were calculated by Kubios HRV 2.1 software. The totally 4707 pairs of RR intervals were analyzed. The average bias between the RR interval, registered by software-hardware complex «Rytm» and «Polar RS800», was only 0.06 s. We revealed the narrow Bland–Altman limits of agreement (3.72 − -3.83 ms) and the highest value of the intraclass correlation coefficient (1.000) between the data of these two devices. The Bland–Altman plot confirmed good agreement between the devices in the measurements of RR intervals. At the same time, the significant difference (p = 0.002) of the two data sets was found according to paired Wilcoxon test. As the final goal of the registration of RR time series is calculation of individual heart rate variability indexes, we intended to test the presence of substantial differences in the heart rate variability indexes, derived from the data from two devices − «Polar RS800» and software-hardware complex «Rytm». We compared the results of time-domain (HR, STD RR, RMSSD, pNN50), frequency-domain (VLF, LF, HF, LF / HF) and nonlinear (RR tri index, SD1, SD2) analysis of heart rate variability. It was found that only for the LF/HF ratio a statistically significant difference was present. Conclusion. The results suggest the good agreement between most of the heart rate variability indexes based on data of software-hardware complex «Rytm» and well approved heart rate monitoring systems («Polar RS800»)


Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Mary Branch ◽  
Christopher L Schaich ◽  
Daniel Beavers ◽  
Elsayed Z Soliman ◽  
Kerryn Reding ◽  
...  

Background: Autonomic dysfunction (AD) as measured by heart rate variability (HRV) is associated with increased risk of cardiovascular disease (CVD) and breast cancer. No study has utilized a large prospective multi-center cohort of diverse women to assess differences in HRV associated with incident breast cancer. Objectives: To identify heart rate variability changes in women with breast cancer compared to controls in the Women’s Health Initiative (WHI). Methods: In a retrospective cohort study, we utilized 5,031 women in the WHI CT cohort who were breast cancer free at baseline and compared 1) those with incident breast cancer v. 2) those who were breast cancer free during the ECG follow-up period as controls. HRV was calculated utilizing 10-second ECG with two measures of two-domain HRV: standard deviation of all normal-to-normal RR intervals (SDNN) and the root mean square of successive differences in normal-to-normal RR intervals (rMSSD). HRV was measured from ECGs collected at baseline, years 3, 6, and 9 in the comparison groups. An adjusted mixed linear model was used to evaluate the differences in SDNN and rMSSD comparing women with incident breast cancer to controls. Cardiovascular risk factors utilized in the adjusted model were determined via questionnaire at baseline. Results: At baseline, women with incident breast cancer diagnosed by years 3, 6, or 9 were significantly older (median age 63 vs. 61, P<0.0001) and had a higher prevalence of hypertension (35% vs. 32%, P=0.02). SDNN at years 3 and 6 in women with breast cancer compared to controls was significantly lower (P=0.0002, P=0.03 respectively). As well, rMSSD was significantly lower at year 3 compared to controls (P<0.0001) ( Figure 1 ). Conclusions: HRV as a measure of AD is significantly lower in women with incident breast cancer compared to women without breast cancer. Reduction in HRV is associated with CVD outcomes in the literature. Our study suggests HRV may predict CVD in breast cancer patients.


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