Impact of heart rate variability,a marker for cardiac health,on lupus disease activity

BackgroundDecreased heart rate variability (HRV) is associated with adverse outcomes in cardiovascular diseases and has been observed in patients with systemic lupus erythematosus (SLE). We examined the relationship of HRV with SLE disease activity and selected cytokine pathways.MethodsFifty-three patients from the Oklahoma Lupus Cohort were evaluated at two visits each. Clinical assessments included the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), British Isles Lupus Assessment Group (BILAG) index, physician global assessment (PGA), and Safety of Estrogens in Lupus Erythematosus National Assessment-SLEDAI Flare Index. HRV was assessed with a 5-minute electrocardiogram, and the following HRV parameters were calculated: square root of the mean of the squares of differences between adjacent NN intervals (RMSSD), percentage of pairs of adjacent NN intervals differing by more than 50 milliseconds (pNN50), high-frequency power (HF power), and low frequency to high frequency (LF/HF) ratio, which reflects sympathetic/vagal balance. Plasma cytokine levels were measured with a multiplex, bead-based immunoassay. Serum B lymphocyte stimulator (BLyS) and a proliferation-inducing ligand (APRIL) were measured with an enzyme-linked immunosorbent assay. Linear regression analysis was applied.ResultsBaseline HRV (pNN50, HF power, LF/HF ratio) was inversely related to disease activity (BILAG, PGA) and flare. Changes in RMSSD between visits were inversely related to changes in SLEDAI (p = 0.007). Age, caffeine, tobacco and medication use had no impact on HRV. Plasma soluble tumor necrosis factor receptor II (sTNFRII) and monokine induced by interferon gamma (MIG) were inversely related with all baseline measures of HRV (p = 0.039 to <0.001). Plasma stem cell factor (SCF), interleukin (IL)-1 receptor antagonist (IL-1RA), and IL-15 showed similar inverse relationships with baseline HRV, and weaker trends were observed for interferon (IFN)-α, interferon gamma-induced protein (IP)-10, and serum BLyS. Changes in the LF/HF ratio between visits were also associated with changes in sTNFRII (p = 0.021), MIG (p = 0.003), IFN-α (p = 0.012), SCF (p = 0.001), IL-1RA (p = 0.023), and IL-15 (p = 0.010). On the basis of multivariate linear regression, MIG was an independent predictor of baseline HRV after adjusting for plasma IL-1RA, SCF, IFN-α, IP-10, and serum BLyS. In a similar model, the sTNFRII impact remained significant after adjusting for the same variables.ConclusionsImpaired HRV, particularly the LF/HF ratio, is associated with lupus disease activity and several cytokines related to IFN type II and TNF pathways. The strongest association was with MIG and sTNFRII, expanding previous immune connections of vagal signaling.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-016-1087-x) contains supplementary material, which is available to authorized users.     

Dimensional analysis of heart rate variability in heart transplant recipients

Periodicities in the heart rate have been known for some time. We discuss these periodicities in normal and transplanted hearts. We then consider the possibility of dimensional analysis of these periodicities in transplanted hearts and problems associated with the record.     

A new algorithm for wavelet-based heart rate variability analysis

One of the most promising non-invasive markers of the activity of the autonomic nervous system is heart rate variability (HRV). HRV analysis toolkits often provide spectral analysis techniques using the Fourier transform, which assumes that the heart rate series is stationary. To overcome this issue, the Short Time Fourier Transform (STFT) is often used. However, the wavelet transform is thought to be a more suitable tool for analyzing non-stationary signals than the STFT. Given the lack of support for wavelet-based analysis in HRV toolkits, such analysis must be implemented by the researcher. This has made this technique underutilized.This paper presents a new algorithm to perform HRV power spectrum analysis based on the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT). The algorithm calculates the power in any spectral band with a given tolerance for the band's boundaries. The MODWPT decomposition tree is pruned to avoid calculating unnecessary wavelet coefficients, thereby optimizing execution time. The center of energy shift correction is applied to achieve optimum alignment of the wavelet coefficients. This algorithm has been implemented in RHRV, an open-source package for HRV analysis. To the best of our knowledge, RHRV is the first HRV toolkit with support for wavelet-based spectral analysis.     

Normalized correlation dimension for heart rate variability analysis.

In this paper we use the concept of large-scale dimension densities to analyze heart rate variability data. This method uses a normalized Grassberger-Procaccia algorithm and estimates the dimension in the rather large scales of the system. This enables us to analyze very short data. First we re-analyze data from the CIC 2002 challenge and can completely distinguish between real data and computer-generated data using only one parameter. We then analyze unfiltered data for 15 patients with atrial fibrillation (AF), 15 patients with congestive heart failure (CHF), 15 elderly healthy subjects, and 18 young healthy subjects. This method can completely separate the AF group from the other groups and the CHF patients show significant differences compared to the young and elderly healthy volunteers. Furthermore, differences are evident in the dimensionality between day and night for healthy persons, but not for the CHF patients. Finally, the results are compared to standard heart rate variability parameters.     

Spectral analysis of the heart rate variability in sleep

Spectral analysis of heart rate variability (HRV) during overnight polygraphic recording was performed in 11 healthy subjects. The total spectrum power, power of the VLF, LF and HF spectral bands and the mean R-R were evaluated. Compared to Stage 2 and Stage 4 non-REM sleep, the total spectrum power was significantly higher in REM sleep and its value gradually increased in the course of each REM cycle. The value of the VLF component (reflects slow regulatory mechanisms, e.g. the renin-angiotensin system, thermoregulation) was significantly higher in REM sleep than in Stage 2 and Stage 4 of non-REM sleep. The LF spectral component (linked to the sympathetic modulation) was significantly higher in REM sleep than in Stage 2 and Stage 4 non-REM sleep. On the contrary, a power of the HF spectral band (related to parasympathetic activity) was significantly higher in Stage 2 and Stage 4 non-REM than in REM sleep. The LF/HF ratio, which reflects the sympathovagal balance, had its maximal value during REM sleep and a minimal value in synchronous sleep. The LF/HF ratio significantly increased during 5-min segments of Stage 2 non-REM sleep immediately preceding REM sleep compared to 5-min segments of Stage 2 non-REM sleep preceding the slow-wave sleep. This expresses the sympathovagal shift to sympathetic predominance occurring before the onset of REM sleep. A significant lengthening of the R-R interval during subsequent cycles of Stage 2 non-REM sleep was documented, which is probably related to the shift of sympathovagal balance to a prevailing parasympathetic influence in the course of sleep. This finding corresponds to a trend of a gradual decrease of the LF/HF ratio in subsequent cycles of Stage 2 non-REM sleep.     

Sample entropy analysis of neonatal heart rate variability

Abnormal heart rate characteristics of reduced variability and transient decelerations are present early in the course of neonatal sepsis. To investigate the dynamics, we calculated sample entropy, a similar but less biased measure than the popular approximate entropy. Both calculate the probability that epochs of window length m that are similar within a tolerance r remain similar at the next point. We studied 89 consecutive admissions to a tertiary care neonatal intensive care unit, among whom there were 21 episodes of sepsis, and we performed numerical simulations. We addressed the fundamental issues of optimal selection of m and r and the impact of missing data. The major findings are that entropy falls before clinical signs of neonatal sepsis and that missing points are well tolerated. The major mechanism, surprisingly, is unrelated to the regularity of the data: entropy estimates inevitably fall in any record with spikes. We propose more informed selection of parameters and reexamination of studies where approximate entropy was interpreted solely as a regularity measure.     

Detection of the third and fourth heart sounds using Hilbert-Huang transform

Background

Magnetic nanoparticles are gaining great roles in biomedical applications as targeted drug delivery agents or targeted imaging contrast agents. In the magnetic nanoparticle applications, quantification of the nanoparticle density deposited in a specified region is of great importance for evaluating the delivery of the drugs or the contrast agents to the targeted tissues. We introduce a method for estimating the nanoparticle density from the displacement of tissues caused by the external magnetic field.

Methods

We can exert magnetic force to the magnetic nanoparticles residing in a living subject by applying magnetic gradient field to them. The nanoparticles under the external magnetic field then exert force to the nearby tissues causing displacement of the tissues. The displacement field induced by the nanoparticles under the external magnetic field is governed by the Navier's equation. We use an approximation method to get the inverse solution of the Navier's equation which represents the magnetic nanoparticle density map when the magnetic nanoparticles are mechanically coupled with the surrounding tissues. To produce the external magnetic field inside a living subject, we propose a coil configuration, the Helmholtz and Maxwell coil pair, that is capable of generating uniform magnetic gradient field. We have estimated the coil currents that can induce measurable displacement in soft tissues through finite element method (FEM) analysis.

Results

From the displacement data obtained from FEM analysis of a soft-tissue-mimicking phantom, we have calculated nanoparticle density maps. We obtained the magnetic nanoparticle density maps by approximating the Navier's equation to the Laplacian of the displacement field. The calculated density maps match well to the original density maps, but with some halo artifacts around the high density area. To induce measurable displacement in the living tissues with the proposed coil configuration, we need to apply the coil currents as big as 10⁴A.

Conclusions

We can obtain magnetic nanoparticle maps from the magnetically induced displacement data by approximating the Navier's equation under the assumption of uniform-gradient of the external magnetic field. However, developing a coil driving system with the capacity of up to 10⁴A should be a great technical challenge.     

Assessment of cardiac autonomic modulation during graded head-up tilt by symbolic analysis of heart rate variability

Two symbolic indexes, the percentage of sequences characterized by three heart periods with no significant variations (0V%) and that with two significant unlike variations (2UV%), have been found to reflect changes in sympathetic and vagal modulations, respectively. We tested the hypothesis that symbolic indexes may track the gradual shift of the cardiac autonomic modulation during an incremental head-up tilt test. Symbolic analysis was carried out over heart period variability series (250 cardiac beats) derived from ECG recordings during a graded head-up tilt test (0, 15, 30, 45, 60, 75, and 90 degrees ) in 17 healthy subjects. The percentage of subjects showing a significant linear correlation (Spearman rank-order correlation) with tilt angles was utilized to evaluate the performance of symbolic analysis. Spectral analysis was carried out for comparison over the same series. 0V% progressively increased with tilt angles, whereas 2UV% gradually decreased. The decline of 2UV% was greater than the increase of 0V% at low tilt angles. Linear correlation with tilt angles was exhibited in a greater percentage of subjects for 0V% and 2UV% than for any spectral index. Our findings suggest that symbolic analysis performed better than spectral analysis and, thus, is a suitable methodology for assessment of the subtle changes of cardiac autonomic modulation induced by a graded head-up tilt test. Moreover, symbolic analysis indicates that the changes of cardiac sympathetic and vagal modulations observed during this protocol were reciprocal but characterized by different absolute magnitudes.     

Quantifying cardiac sympathetic and parasympathetic nervous activities using principal dynamic modes analysis of heart rate variability

The ratio between low-frequency (LF) and high-frequency (HF) spectral power of heart rate has been used as an approximate index for determining the autonomic nervous system (ANS) balance. An accurate assessment of the ANS balance can only be achieved if clear separation of the dynamics of the sympathetic and parasympathetic nervous activities can be obtained, which is a daunting task because they are nonlinear and have overlapping dynamics. In this study, a promising nonlinear method, termed the principal dynamic mode (PDM) method, is used to separate dynamic components of the sympathetic and parasympathetic nervous activities on the basis of ECG signal, and the results are compared with the power spectral approach to assessing the ANS balance. The PDM analysis based on the 28 subjects consistently resulted in a clear separation of the two nervous systems, which have similar frequency characteristics for parasympathetic and sympathetic activities as those reported in the literature. With the application of atropine, in 13 of 15 supine subjects there was an increase in the sympathetic-to-parasympathetic ratio (SPR) due to a greater decrease of parasympathetic than sympathetic activity (P=0.003), and all 13 subjects in the upright position had a decrease in SPR due to a greater decrease of sympathetic than parasympathetic activity (P<0.001) with the application of propranolol. The LF-to-HF ratio calculated by the power spectral density is less accurate than the PDM because it is not able to separate the dynamics of the parasympathetic and sympathetic nervous systems. The culprit is equivalent decreases in both the sympathetic and parasympathetic activities irrespective of the pharmacological blockades. These findings suggest that the PDM shows promise as a noninvasive and quantitative marker of ANS imbalance, which has been shown to be a factor in many cardiac and stress-related diseases.     

Hilbert-Huang transform in MHD plasma diagnostics

A new method for processing experimental data from MHD diagnostics is discussed that provides a more detailed study of the dynamics of large-scale MHD instabilities. The method is based on the Hilbert-Huang transform method and includes an empirical mode decomposition algorithm, which is used to decompose the experimental MHD diagnostic signals into a set of frequency-and amplitude-modulated harmonics in order to construct the time evolutions of the amplitudes and frequencies of these harmonics with the help of the Hilbert transform. The method can also be applied to analyze data from other diagnostics that measure unsteady oscillating signals.     

糖尿病植物神经病变病人心率变异的非线性定量分析

目的：研究糖尿病人植物神经病变与心率变异的关系。对象：正常对照组和根据临床有无糖尿病神经病变（ＤＡＮ）分组的糖尿病病人,方法：应用２４小时动态心电图对正常和糖尿病人进行心率变异的线性,非线性散点图和非线性定量参数分析,结果：单纯糖尿病组ＳＤＮＮ,ＳＤＡＮＮ和ＰＮＮ５０低于正常组（Ｐ〈０．０５）;糖尿病＋ＤＡＮ组各项线性时域分析指标均低于正常和单纯糖尿病组（Ｐ〈０．０１－０．００１）,散点图分析结果     

Association of health behaviour with heart rate variability: a population-based study

Background

Multiple randomized controlled trials (RCTs) have examined the cardiovascular effects of omega-3 fatty acids and have provided unexplained conflicting results. A meta-analysis of these RCTs to estimate efficacy and safety and potential sources of heterogeneity may be helpful.

Methods

The Cochrane library, MEDLINE, and EMBASE were systematically searched to identify all interventional trials of omega-3 fatty acids compared to placebo or usual diet in high-risk cardiovascular patients. The primary outcome was all-cause mortality and secondary outcomes were coronary restenosis following percutaneous coronary intervention and safety. Meta-analyses were carried out using Bayesian random-effects models, and heterogeneity was examined using meta-regression.

Results

A total of 29 RCTs (n = 35,144) met our inclusion criteria, with 25 reporting mortality and 14 reporting restenosis. Omega-3 fatty acids were not associated with a statistically significant decreased mortality (relative risk [RR] = 0.88, 95% Credible Interval [CrI] = 0.64, 1.03) or with restenosis prevention (RR = 0.89, 95% CrI = 0.72, 1.06), though the probability of some benefit remains high (0.93 and 0.90, respectively). However in meta-regressions, there was a >90% probability that larger studies and those with longer follow-up were associated with smaller benefits. No serious safety issues were identified.

Conclusions

Although not reaching conventional statistical significance, the evidence to date suggests that omega-3 fatty acids may result in a modest reduction in mortality and restenosis. However, caution must be exercised in interpreting these benefits as results were attenuated in higher quality studies, suggesting that bias may be at least partially responsible. Additional high quality studies are required to clarify the role of omega-3 fatty acid supplementation for the secondary prevention of cardiovascular disease.     

A refined method of quantifying deceleration capacity index for heart rate variability analysis

Background

Phase-rectified signal averaging (PRSA) was often applied to assess the cardiac vagal modulation. Despite its broad use, this method suffers from the confounding effects of anomalous variants of sinus rhythm. This study aimed to improve the original PRSA method in deceleration capacity (DC) quantification.

Methods

The refined deceleration capacity (DC_ref) was calculated by excluding from non-vagally mediated abnormal variants of sinus rhythms. Holter recordings from 202 healthy subjects and 51 patients with end-stage renal disease (ESRD) have been used for validity. The DC_ref was compared to original DC (DC_org) by the area under receiver operating characteristic curve.

Results

Experimental results demonstrate that the original and refined DCs calculated from 24-h, 2-h, and 30-min Holter recordings are significantly lower in patients with ESRD than those in the healthy group. In receiver operating characteristic curve analysis, the DC_ref provides better performance than the DC_org in distinguishing between the patients with ESRD and healthy control subjects. Furthermore, the refined PRSA technique enhances the low frequency and attenuates high frequency components for spectral analysis in ESRD patients.

Conclusions

The DC_ref appears to reduce the influence of non-vagally mediated abnormal variants of sinus rhythm and highlighting the pathological influence. DC_ref, especially assessed from short-term electrocardiography recordings, may be complementary to existing autonomic function assessment, risk stratification, and efficacy prediction strategies.

     

Evaluating and comparing performance of feature combinations of heart rate variability measures for cardiac rhythm classification

Automatic classification of cardiac arrhythmias using heart rate variability (HRV) analysis has been an important research topic in recent years. Explorations reveal that various HRV feature combinations can provide highly accurate models for some rhythm disorders. However, the proposed feature combinations lack a direct and carefully designed comparison. The goal of this work is to assess the various HRV feature combinations in classification of cardiac arrhythmias. In this setting, a total of 56 known HRV features are grouped in eight feature combinations. We evaluate and compare the combinations on a difficult problem of automatic classification between nine types of cardiac rhythms using three classification algorithms: support vector machines, AdaBoosted C4.5, and random forest. The effect of analyzed segment length on classification accuracy is also examined. The results demonstrate that there are three combinations that stand out the most, with total classification accuracy of roughly 85% on time segments of 20 s duration. A simple combination of time domain features is shown to be comparable to the more informed combinations, with only 1–4% worse results on average than the three best ones. Random forest and AdaBoosted C4.5 are shown to be comparably accurate, while support vector machines was less accurate (4–5%) on this problem. We conclude that the nonlinear features exhibit only a minor influence on the overall accuracy in discerning different arrhythmias. The analysis also shows that reasonably accurate arrhythmia classification lies in the range of 10–40 s, with a peak at 20 s, and a significant drop after 40 s.     

Wavelet analysis of nonstationary heart rate variability in a head-up tilt-table test

Heart rate variability during the head-up tilt test (HUT) was investigated using an automated setup of dynamic positioning in the gravity field. The wavelet theory was applied to find the dynamics of changes in the spectral properties of rhythmograms during the transitional steps of HUT. Quantitative parameters (spectral integrals, instantaneous maximal frequencies, nonstationarity factors) that describe the transients of such nonstationary HUT were calculated.     

Spectral analysis and interpretation of spectral components of heart rate variability

Requirements for a fragment of a random process taken for spectral analysis are formulated. It has been demonstrated that both the random process and its periodic components should be stationary within the fragment selected. Only in this case will the results of spectral analysis reflect the actual characteristics of the process studied. Variations in the depth of breathing and other factors have been shown to cause modulatory fluctuations of the HF and LF waves of the cardiac rhythm. The modulatory fluctuations are expressed as the VLF peak in the heart rate variability spectrum, the position of the peak on the frequency axis and its amplitude being determined by the frequency and depth of modulation, respectively. Depending on the modulation depth and the ratio between the modulation period and the length of the sample studied, spectral analysis of the same process may yield considerably different results. The ambiguity of the results of spectral analysis is the cause of discrepancy about the nature of individual spectral components of heart rate variability.     

Mathematical model of heart rate variability

The study presents a mathematical model of non-linear dynamics of the heart rate variability (HRV). The model is based on quantitative characteristics of pulse conduction in the heart conducting system: the delays of sinoatrial (SA) and atrioventricular (AV) pulse conduction and refractors periods of the SA and AV nodes. The model predicts heart rate disturbances in fast electric activity of the atria, increase in the delay of the AV conduction, the critical value of atrial period where transition to non-linear dynamics of the heart rate variability starts. The correlation between indexes of HRV and period of stimulation of atria for 1-contour cardiac control model has been demonstrated.     

Spectral analysis of heart rate variability during constant and pseudorandom exercise

The purpose of this study was to investigate the limitation for applying a linear model to the cardiorespiratory control system. Four subjects performed the two types of exercise bouts, constant (CONST) and pseudorandom (PRBS) exercise, on an electrically braked cycle ergometer at three different work rates. The target work rate of CONST were set to 80, 100, 120% of the individual anaerobic threshold (AT). In PRBS, the work rates were varied between +/- 10% of the individual AT around the respective target work rates of CONST. Although the spectral density of beat-to-beat heart rate fluctuations showed the conventional patterns for most cases, there was no obvious difference between CONST and PRBS. These results indicated that the variation of +/- 10% of AT did not affect the heart rate variation as the output response, suggesting a dilemma inevitable to apply a linear model based on the transfer function.     

The application of surface plethysmography for heart rate variability analysis after GSM radiofrequency exposure

The aim of the present study was to test whether the electromagnetic field emitted by standard GSM mobile phones results in changes in heart rate (HR) and heart rate variability (HRV) of 35 healthy young male and female subjects. Two parallel signals, electrocardiogram and infrared surface plethysmogram were recorded and compared to test their validity for the analysis. Plethysmographic recording is proved to be a fast and reliable method for HRV measurements. In the radiofrequency (RF) exposure study, there was no significant difference in the values of HR and HRV between the RF and the sham groups. Our preliminary study demonstrates that, in our experimental conditions, RF fields emitted by cellular phones do not cause observable effects on the regulation of heart rate of healthy, young adults.