10.6 Comparison of fft and DFT algorithms in calculation of heart rate variability spectral parameters

EP Europace ◽  
2003 ◽  
Vol 4 ◽  
pp. A17
Author(s):  
K MUS
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Spectral Parameters

Effect of amiodarone on heart rate variability in patients with newly diagnosed atrial fibrillation (clinical observation)

2020 ◽  
pp. 81-85
Author(s):  
E. P. Popova ◽  
O. T. Bogova ◽  
S. N. Puzin ◽  
D. A. Sychyov ◽  
V. P. Fisenko
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Drug Therapy ◽  
Heart Function ◽  
Newly Diagnosed ◽  
Class Iii ◽  
Spectral Parameters ◽  
Patient Will ◽  
The Individual

Spectral analysis of heart rate variability gives an idea of the role of the autonomic nervous system in the regulation of chronotropic heart function. This method can be used to evaluate the effectiveness of drug therapy. Drug therapy should be carried out taking into account the individual clinical form of atrial fibrillation. Information about the vegetative status of the patient will undoubtedly increase the effectiveness of treatment. In this study, spectral parameters were studied in patients with newly diagnosed atrial fibrillation. The effect of antiarrhythmic drug class III amiodarone on the spectral parameters of heart rate variability was studied.

Monitoring Heart Rate Variability As A Biomarker Of Fatigue In Young Athletes

2020 ◽  
Author(s):  
Martina Bernaciková ◽  
Jakub Mazúr ◽  
Martin Sebera ◽  
Petr Hedbávný
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
High Performance ◽  
Transition Period ◽  
Training Volume ◽  
Young Athletes ◽  
Spectral Parameters ◽  
Significance Level ◽  
Sports Science ◽  
Spectral Performance

Purpose: Many high performance and especially top athletes are still at risk or suffer from total fatigue. Therefore, sports science seeks to develop an objective, sensitive and reliable method of early diagnosis of this fatigue (e.g. heart rate variability – HRV as a modern ob-jective method). The aim of the study was to evaluate whether the HRV monitoring could be a complementary diagnostic tool for overreaching / overtraining in young athletes. Already introduced “classical” indicators of HRV, such as spectral performance and its density in the established frequency ranges, are a part of athlete monitoring in the scope of overreaching prevention We were monitoring the heart rate variability parameters at three different phases of the year-long training cycle and to find out whether in one of these phases we could find athletes showing symptoms of overreaching. Methods: 48 young athletes (33 boys 14.8 ± 1.5 years, 15 girls 14.9 ± 1.7 years) were involved in the study, consisting of 38 boys and 10 girls. There were 15 swimmers (with training volume 9x 1.5‒2 hours a week), 12 artistic gymnasts (with training volume 9x 2‒2.5 hours a week) and 21 badminton players (with training volume 4x weekly 1.5‒2 hours a week). Monitoring was carried out in athletes in three training periods: at the end of the transition period, at the end of the prepared period, at the end of the competition period. Measurements were carried out in the morning. The DiANS PF8 system was used to measure the heart rate variability, the measurements were performed at five-minute intervals: lying-standing-lying. Time and spectral parameters of HRV were monitored. Results: Results of HRV in three periods (HR + rMSSD in lying). Boys: HR (61 ± 8, 64 ± 7, 64 ± 8), rMSSD (85 ± 64; 80 ± 54; 88 ± 59), TS (-0.56 ± 1.53; -0.87 ± 1.4; -0.42 ± 1.44). Girls: HR (65 ± 8; 64 ± 7; 65 ± 8), rMSSD (74 ± 37; 79 ± 35; 83 ± 43), TS (-0.58 ± 1.57; -0.72 ± 1.35); -0.18 ± 0.18). Statistically significant differences (at the significance level = 0.05) among sports were found in Kruskal-Walls ANOVAby Ranks: boys in LF-standing, HF standing, FV, SVB and TS; girls in HF-lying, HF-standing, rMSSD, TP-lying, TP-standing, FV, VA and TS. Conclusion: Monitoring of heart rate variability seems to be a practical tool for prevention of overtraining even in young age. To monitor heart rate variability, we recommend monitoring these parameters: RR, rMSSD, VA, SVB, TS.

Time-variant heart-rate variability spectral parameters during dobutamine test

2002 ◽  
Author(s):  
L.T. Mainardi ◽  
E. Petrucci ◽  
V. Balian ◽  
A.M. Bianchi ◽  
A. Porta ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Spectral Parameters

scholarly journals FREQUENCY ANALYSIS OF CARDIAC DATA OBTAINED THROUGH HOLTER MONITORING IN REAL LIVING CONDITIONS

2019 ◽  
Vol 7 ◽  
pp. 870-874
Author(s):  
Galya Nikolova Georgieva-Tsaneva
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
High Frequency ◽  
Scientific Literature ◽  
Real Life ◽  
Low Frequency ◽  
Holter Monitoring ◽  
Heart Rate Variability Analysis ◽  
Healthy Individuals ◽  
Spectral Parameters

The paper presents frequency methods for estimating the variability of intervals between individual heart beats in Electrocardiogram. This parameter is known in the scientific literature as the Heart Rate Variability and with this method it is possible to make predictions about human health. Three frequency ranges have been studied: Very Low Frequency, Low Frequency, and High Frequency. The study in this paper was based on real cardiological data obtained from 33 patients suffering from heart fibrillations and 29 healthy individuals. The investigated records are obtained through a Holter monitoring of studied individuals in real life conditions. The obtained results show significantly lower values ​​of the tested spectral parameters in the diseased individuals compared to the healthy controls. The accomplished study shows the effective applicability of the spectral methods of Heart Rate Variability analysis and the possibility of differentiation by the spectral parameters of the patients from healthy individuals.

On-Line Beat-to-Beat Monitoring of Spectral Parameters of Heart Rate Variability Signal Using a Pole-Tracking Algorithm

1994 ◽  
Vol 33 (01) ◽  
pp. 85-88
Author(s):  
A. M. Bianchi ◽  
S. Cerutti ◽  
L. T. Mainardi
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Ar Model ◽  
Model Parameters ◽  
Spectral Parameters ◽  
On Line ◽  
Model Transfer ◽  
Stationary Conditions ◽  
Simulated Time ◽  
Simulated Time Series

Abstract:Spectral parameters extracted from the heart rate variability (HRV) signal are obtained on a beat-to-beat basis, following a procedure which uses two recursive algorithms. In the first step of the procedure the set of the AR model coefficients is updated each time a new RR value is available. Then from the estimated AR model parameters, the new position of the poles of the model transfer function in the complex z-plane is evaluated and, finally, through a residual calculation, it is possible to calculate the spectral parameters which quantify the control of the autonomic nervous system in assessing the cardiac frequency (i.e., power and frequency of LF and HF components). The whole procedure has first been tested on a simulated time series, in order to evaluate its performance in tracking the dynamic changes during different conditions; next the algorithms were employed in the study of the HRV signal for continuous monitoring of non-stationary conditions.

scholarly journals INFLUENCE OF THE TOTAL POWER OF THE HEART RATE VARIABILITY SPECTRUM ON THE SPECTRAL PARAMETERS DISTRIBUTION IN PATIENTS WITH ARTERIAL HYPERTENSION IN A PACED BREATHING TEST

2018 ◽  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Arterial Hypertension ◽  
Cardiac Activity ◽  
Heart Rhythm ◽  
Total Power ◽  
Spectral Parameters ◽  
Paced Breathing ◽  
Software Complex ◽  
Heart Rate Variability Spectrum

To determine the effect of the total power (TP) of the heart rate variability (HRV) spectrum on the distribution of high, low and very low frequency waves, 40 patients with arterial hypertension (AH) at the age of 58 ± 9 years were divided into 5 groups according to the degree of TP decrease in the initial stage of the test: 1st – more than 3000 ms2; 2nd – 3000–2000 ms2; 3rd – 2000-1000 ms2; 4th – 1000–500 ms2; 5th – less than 500 ms2. To assess HRV parameters in each group, 3 stages of the paced breathing test with a double (light and sound) metronome were evaluated; the hardware and software complex «Cardiolab» («HAI-Medica») was used. The distribution of the parameters was estimated taking into account the median, 25 and 75 quartiles. To estimate the differences between the statistical samples, the nonparametric Mann-Whitney U-test was used, as well as the Craskell–Wallis criterion. Statistically significant differences were considered between the data at a value of p < 0.05. It was found that the greater is the degree of TP reduction, the more significant is the autonomic imbalance, as well as the decrease in the influence of paced breathing on the regulation of the heart rhythm; at TP values below 1000 ms2 not only the parasympathetic component decrease is observed, but also the transition from sympathicotonia to the neurohumoral factors prevalence. In patients with arterial hypertension, there is a tendency of decrease in the total power of the HRV spectrum, thus reflecting the decreased functional capacity of heart rhythm regulation.The lower the degree of TP, the more significant is the disturbance of HRV regulation with a decrease in the parasympathetic component of the heart rate variability spectrum and the dominant influence of sympathetic and neurohumoral factors.The influence of the paced breathing on the heart rhythm regulation falls depending on the decrease in the total power of the HRV spectrum: at TP values below 1000 ms2 not only the parasympathetic component decrease is observed, but also the transition from sympathotonia to the neurohumoral factors prevalence.Decrease in TP can be considered as an indicator of aggravation of autonomic and neurohumoral regulation.Thepaced breathing test allows determine the basic level of cardiac activity regulation and dynamic disruptions in the distribution of HRV components in the metronomized breathing, as well as the possibilities for restoring the regulatory balance of heart rate variability, which is especially important in the examination of patients with arterial hypertension.

Automatic Decomposition of Wigner Distribution and its Application to Heart Rate Variability

2004 ◽  
Vol 43 (01) ◽  
pp. 17-21 ◽  
Author(s):  
N. Montano ◽  
S. Cerutti ◽  
L. T. Mainardi
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wigner Distribution ◽  
Early Response ◽  
Least Square ◽  
Spectral Parameters ◽  
Time Frequency ◽  
Novel Approach ◽  
Stationary Signals ◽  
Automatic Decomposition

Summary Objective: We introduce an algorithm for the automatic decomposition of Wigner Distribution (WD) and we applied it for the quantitative extraction of Heart Rate Variability (HRV) spectral parameters during non-stationary events. Early response to tilt was investigated. Methods: Quantitative analysis of multi-components non-stationary signals is obtained through an automatic decomposition of WD based on least square (LS) fitting of the instantaneous autocorrelation function (ACF). Through this approach the different signal and interference terms which contributes to the ACF may be separated and their parameters (instantaneous frequency and amplitude) quantified. A beat-to-beat monitoring of HRV spectral components is obtained. Results: Analysis of simulated signals demonstrated the capability of the proposed approach to track and separate the signal components. Analysis of HRV data evidenced different dynamics in the early Autonomic Nervous System (ANS) response to tilt. Conclusions: The novel approach to the quantification of the beat-to-beat HRV spectral parameters obtained from decomposition of Wigner distribution was demonstrated to be effective in the analysis of HRV data. Relevant physiological information about the dynamics of the early sympathetic response to tilt were obtained. The method is a general approach which may be employed for a quantitative time-frequency analysis of non-stationary biological signals.

scholarly journals ANALYSIS AND FORECASTING OF RESERVE CAPABILITIES OF THE ORGANISM OF STUDENTS ACCORDING TO INDICES OF HEART RATE VARIABILITY

2019 ◽  
Vol 96 (6) ◽  
pp. 556-561 ◽  
Author(s):  
Irina G. Kretova ◽  
O. A. Vedyasova ◽  
M. V. Komarova ◽  
O. I. Shiryaeva
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Cardiovascular System ◽  
Heart Rate Recovery ◽  
Stress Test ◽  
Autonomic Regulation ◽  
Cardiac Response ◽  
Operating Characteristics ◽  
Spectral Parameters ◽  
Heart Rate Regulation

Introduction. Currently, there is a deterioration trend in the health of the younger generation, in particular, an increasing number of persons with functional disorders of the cardiovascular system. Regarding this it is important to develop an comprehensive approach to the study of the circulatory indices in the young age with informative clinical diagnostic techniques and new ways to analyze the data. Material and methods. Heart rate variability (HRV) indices in 200 students at rest and during exercise were studied. For a more complete assessment of the functional reserves of cardiovascular system after the load the index of the normal heart rhythm restoration (SDNN) was calculated. Moreover, we built logistic models and curves of the operating characteristics. Results. At rest, no significant differences in indices of both HRV and autonomic regulation of heart rate have been identified between boys and girls aged of 16-18 years. Students of the different gender, aged of 19-22 years showed significant differences in spectral parameters of HRV, there was noted the dominance of the sympathetic component of heart rate regulation in boys and parasympathetic - in girls. In terms of physical activity in undergraduate students there is observed mainly the activation of vagal influences on the heart, in senior students the vegetative balance shifted to the direction of the increased activity of mechanisms of the sympathetic regulation. The revealed decrease of NHRR reflects the low level of the reserve capacity of the cardiovascular system in 41% out of observed students. Part of students with an increased probability of the reduction of functional cardiac disorders accounted for 42% of boys and 39% of girls among students aged 16-18 years and 36% of boys and 44% of girls among students aged of 19-22 years. Conclusion. There is a change in the nature of autonomous regulation of heart rate in students of different gender and ages from 16-18 to 19-22 years. The optimal cardiac response to stress test is observed at low baseline values of heart rate and the prevalence of the parasympathetic part of the autonomic regulation of the heart rate at rest. To assess the functional reserve of the cardiovascular system in HRV indices we recommend the calculation of NHRR and the analysis of the SDNN coefficient for the effective prediction of heart rate recovery rate after exercise testing.

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