Traditional Heart Rate Variability Indices Research Articles

Correlation among Poincare plot and traditional heart rate variability indices in adults with different risk levels of metabolic syndrome: a cross-sectional approach from Southern India.

Heart rate variability (HRV) is an important marker of cardiac autonomic modulation. Metabolic syndrome (MetS) can alter cardiac autonomic modulation, raising the risk of cardiovascular disease (CVD). Poincaré plot analysis (PPA) is a robust scatter plot-based depiction of HRV and carries similar information to the traditional HRV measures. However, no prior studies have examined the relationship between PPA and traditional HRV measures among different risk levels of MetS. We evaluated the association between the Poincare plot and traditional heart rate variability indices among adults with different risk levels of MetS. We measured anthropometric data and collected fasting blood samples to diagnose MetS. The MetS risk was assessed in 223 participants based on the number of MetS components and was classified as control (n=64), pre-MetS (n=49), MetS (n=56), and severe MetS (n=54). We calculated the Poincaré plot (PP) and traditional HRV measures from a 5min HRV recording. Besides the traditional HRV measures, we found that various HRV indices of PPA showed significant differences among the groups. The severe MetS group had significantly lower S (total HRV), SD1 (short-term HRV), SD2 (long-term HRV), and higher SD2/SD1. The values of S, SD1, SD2, and SD2/SD1 were significantly correlated with most traditional HRV measures. We found gradual changes in HRV patterns as lower parasympathetic and higher sympathetic activity alongside the rising number of MetS components. The HRV indices of PPA integrating the benefits of traditional HRV indices distinguish successfully between different risk levels of MetS and control subjects.

Heart rate variability in athletes with symptoms of non-functional overload

The article examines the effect on heart rate variability (HRV) indices in athletes depending on training status, different types of physical exertion, gender and age, presented in both cross-sectional and longitudinal studies. Also included is the predictability of HRV during overtraining, athletic condition, and athletic performance. Heart rate variability (HRV) has long been used as a diagnostic marker of overexertion and overtraining. A large body of evidence shows that in athletes of all ages, regular aerobic training usually results in significant improvements in total and instantaneous HRV. These changes, which are accompanied by a significant decrease in heart rate both at rest and during submaximal exercise, reflect an increase in autonomic efferent activity and a shift in favor of enhanced vagal modulation of heart rate. At present, the available data do not allow us to draw definitive conclusions about the usefulness of traditional HRV indices for evaluating the performance of physical exercises and monitoring the training load. The discrepancies in the results published so far are due to several factors, including insufficient study size and design, and different HRV methods. It also remains to be seen whether traditional HRV indices will be useful for diagnosing overexertion and overtraining.

Decomposing the complexity of heart-rate variability by the multifractal–multiscale approach to detrended fluctuation analysis: an application to low-level spinal cord injury

Objective: While several studies have assessed autonomic cardiovascular control after a spinal cord lesion using heart-rate variability (HRV) indices in the frequency and time domains, complexity measures have rarely been used, even if detrended fluctuation analysis (DFA) appeared promising. Recent developments in DFA decompose the multifractal contributions using temporal scales. Our aim is to evaluate the potential of these new DFA tools, considering as an example application the decomposition of HRV complexity in individuals with spinal cord injury (SCI) at a low lesion level, for whom alterations in traditional indices are not expected. Approach: We enrolled 14 subjects with SCI with a lesion below the eleventh thoracic vertebra and 34 able-bodied (AB) controls. We recorded the R–R intervals (RRI) for 10 min in supine and sitting postures. We applied the multifractal–multiscale (MFMS) DFA to derive scale coefficients, α(q,τ), with function of the multifractal order q and scale τ, and evaluated a scale-coefficient dispersion index, αSD(τ), as the standard deviation of α(q,τ) over q. We calculated the RRI increments, their magnitude and sign, estimating the MFMS DFA coefficients for the series of magnitude αm(q,τ) and sign αs(q,τ). Main results: While sitting, differences between SCI and AB groups depended on q for coefficients 16 < τ < 32 s, so that αSD(τ) was lower in individuals with SCI at τ = 25 s. In the supine condition, short-term scales were greater in individuals with SCI for all q, and αSD(τ) did not differ between groups. Group differences were found in αs(q,τ) and not in αm(q,τ) or in traditional HRV indices. The surrogate analysis showed AB–SCI differences in linear HRV components at scales τ < 16 s and nonlinear components at larger scales. Significance: Complexity decomposition by DFA describes autonomic alterations in HRV in low-level paraplegia better than traditional indices, probably pointing out a loss of system complexity in the sitting posture and an impaired sympatho/vagal modulation in the supine position.

CRSIDLab: A Toolbox for Multivariate Autonomic Nervous System Analysis Using Cardiorespiratory Identification.

This paper presents the Cardiorespiratory System Identification Lab (CRSIDLab), a MATLAB-based software tool for multivariate autonomic nervous system (ANS) evaluation through heart rate variability (HRV) analysis and cardiorespiratory system identification. Based on a graphical user interface, CRSIDLab provides a complete set of tools including pre-processing cardiorespiratory data (electrocardiogram, continuous blood pressure, airflow, and instantaneous lung volume), power spectral density estimation, and multivariable cardiorespiratory system model identification. Parametrized multivariate models can assess both HRV and baroreflex sensitivity (BRS) by considering the causal relationship from respiration to heart rate (or its reciprocal, R-to-R interval - RRI) and from systolic blood pressure to RRI, for instance. The impulse response, estimated from the model, is used as a mathematical tool to effectively open the inherently closed-loop nature of the cardiorespiratory system, allowing the investigation of the dynamic response between pairs of cardiorespiratory variables. This system modeling approach provides information on gain and temporal behavior regarding dynamics, such as the baroreflex, complementing traditional HRV, and BRS indices. The toolbox is presented and used to investigate autonomic function in sleep apnea. The results show that, while traditional HRV indices were unable to differentiate between apneic and non-apneic subjects, the autonomic descriptors obtained from the multivariate system identification techniques were able to show vagal impairment in apneic compared to non-apneic subjects. Thus, CRSIDLab can help promote the use of cardiorespiratory system identification as a potentially more sensitive measure of ANS activity than classical HRV analysis.

Heart Rate Fragmentation: A New Approach to the Analysis of Cardiac Interbeat Interval Dynamics.

Background: Short-term heart rate variability (HRV) is most commonly attributed to physiologic vagal tone modulation. However, with aging and cardiovascular disease, the emergence of high short-term HRV, consistent with the breakdown of the neuroautonomic-electrophysiologic control system, may confound traditional HRV analysis. An apparent dynamical signature of such anomalous short-term HRV is frequent changes in heart rate acceleration sign, defined here as heart rate fragmentation.Objective: The aims were to: (1) introduce a set of metrics designed to probe the degree of sinus rhythm fragmentation; (2) test the hypothesis that the degree of fragmentation of heartbeat time series increases with the participants' age in a group of healthy subjects; (3) test the hypothesis that the heartbeat time series from patients with advanced coronary artery disease (CAD) are more fragmented than those from healthy subjects; and (4) compare the performance of the new fragmentation metrics with standard time and frequency domain measures of short-term HRV.Methods: We analyzed annotated, open-access Holter recordings (University of Rochester Holter Warehouse) from healthy subjects and patients with CAD using these newly introduced metrics of heart rate fragmentation, as well as standard time and frequency domain indices of short-term HRV, detrended fluctuation analysis and sample entropy.Results: The degree of fragmentation of cardiac interbeat interval time series increased significantly as a function of age in the healthy population as well as in patients with CAD. Fragmentation was higher for the patients with CAD than the healthy subjects. Heart rate fragmentation metrics outperformed traditional short-term HRV indices, as well as two widely used nonlinear measures, sample entropy and detrended fluctuation analysis short-term exponent, in distinguishing healthy subjects and patients with CAD. The same level of discrimination was obtained from the analysis of normal-to-normal sinus (NN) and cardiac interbeat interval (RR) time series.Conclusion: The fragmentation framework and accompanying metrics introduced here constitute a new way of assessing short-term HRV under free-running conditions, one which appears to overcome salient limitations of traditional HRV analysis. Fragmentation of sinus rhythm cadence may provide new dynamical biomarkers for probing the integrity of the neuroautonomic-electrophysiologic network controlling the heartbeat in health and disease.

ConceFT for Time-Varying Heart Rate Variability Analysis as a Measure of Noxious Stimulation During General Anesthesia.

Heart rate variability (HRV) offers a noninvasive way to peek into the physiological status of the human body. When this physiological status is dynamic, traditional HRV indices calculated from power spectrum do not resolve the dynamic situation due to the issue of nonstationarity. Clinical anesthesia is a typically dynamic situation that calls for time-varying HRV analysis. Concentration of frequency and time (ConceFT) is a nonlinear time-frequency (TF) analysis generalizing the multitaper technique and the synchrosqueezing transform. The result is a sharp TF representation capturing the dynamics inside HRV. Companion indices of the commonly applied HRV indices, including time-varying low-frequency power (tvLF), time-varying high-frequency power, and time-varying low-high ratio, are considered as measures of noxious stimulation. To evaluate the feasibility of the proposed indices, we apply these indices to study two different types of noxious stimulation, the endotracheal intubation and surgical skin incision, under general anesthesia. The performance was compared with traditional HRV indices, the heart rate reading, and indices from electroencephalography. The results indicate that the tvLF index performs best and outperforms not only the traditional HRV index, but also the commonly used heart rate reading. With the help of ConceFT, the proposed HRV indices are potential to provide a better quantification of the dynamic change of the autonomic nerve system. Our proposed scheme of time-varying HRV analysis could contribute to the clinical assessment of analgesia under general anesthesia.

Effects of breathing patterns and light exercise on linear and nonlinear heart rate variability.

Despite their use in cardiac risk stratification, the physiological meaning of nonlinear heart rate variability (HRV) measures is not well understood. The aim of this study was to elucidate effects of breathing frequency, tidal volume, and light exercise on nonlinear HRV and to determine associations with traditional HRV indices. R-R intervals, blood pressure, minute ventilation, breathing frequency, and respiratory gas concentrations were measured in 24 healthy male volunteers during 7 conditions: voluntary breathing at rest, and metronome guided breathing (0.1, 0.2 and 0.4 Hz) during rest, and cycling, respectively. The effect of physical load was significant for heart rate (HR; p < 0.001) and traditional HRV indices SDNN, RMSSD, lnLFP, and lnHFP (p < 0.01 for all). It approached significance for sample entropy (SampEn) and correlation dimension (D2) (p < 0.1 for both), while HRV detrended fluctuation analysis (DFA) measures DFAα1 and DFAα2 were not affected by load condition. Breathing did not affect HR but affected all traditional HRV measures. D2 was not affected by breathing; DFAα1 was moderately affected by breathing; and DFAα2, approximate entropy (ApEn), and SampEn were strongly affected by breathing. DFAα1 was strongly increased, whereas DFAα2, ApEn, and SampEn were decreased by slow breathing. No interaction effect of load and breathing pattern was evident. Correlations to traditional HRV indices were modest (r from -0.14 to -0.67, p < 0.05 to <0.01). In conclusion, while light exercise does not significantly affect short-time HRV nonlinear indices, respiratory activity has to be considered as a potential contributor at rest and during light dynamic exercise.

Fetal autonomic brain age scores, segmented heart rate variability analysis, and traditional short term variability.

Disturbances of fetal autonomic brain development can be evaluated from fetal heart rate patterns (HRP) reflecting the activity of the autonomic nervous system. Although HRP analysis from cardiotocographic (CTG) recordings is established for fetal surveillance, temporal resolution is low. Fetal magnetocardiography (MCG), however, provides stable continuous recordings at a higher temporal resolution combined with a more precise heart rate variability (HRV) analysis. A direct comparison of CTG and MCG based HRV analysis is pending. The aims of the present study are: (i) to compare the fetal maturation age predicting value of the MCG based fetal Autonomic Brain Age Score (fABAS) approach with that of CTG based Dawes-Redman methodology; and (ii) to elaborate fABAS methodology by segmentation according to fetal behavioral states and HRP. We investigated MCG recordings from 418 normal fetuses, aged between 21 and 40 weeks of gestation. In linear regression models we obtained an age predicting value of CTG compatible short term variability (STV) of R2 = 0.200 (coefficient of determination) in contrast to MCG/fABAS related multivariate models with R2 = 0.648 in 30 min recordings, R2 = 0.610 in active sleep segments of 10 min, and R2 = 0.626 in quiet sleep segments of 10 min. Additionally segmented analysis under particular exclusion of accelerations (AC) and decelerations (DC) in quiet sleep resulted in a novel multivariate model with R2 = 0.706. According to our results, fMCG based fABAS may provide a promising tool for the estimation of fetal autonomic brain age. Beside other traditional and novel HRV indices as possible indicators of developmental disturbances, the establishment of a fABAS score normogram may represent a specific reference. The present results are intended to contribute to further exploration and validation using independent data sets and multicenter research structures.

Cardiovascular autonomic function analysis using approximate entropy from 24-h heart rate variability and its frequency components in patients with type 2 diabetes.

Aims/IntroductionThe principal aim of the present study was to investigate the cardiovascular autonomic system status of diabetes patients using approximate entropy (ApEn) extracted from 24-h heart rate variability (HRV) and its frequency components.Materials and MethodsA total of 29 healthy controls and 63 type 2 diabetes patients were included. Participants’ 24-h HRV signals were recorded, and decomposed and reconstructed into four frequency components: high, low, very low and ultra low. The total 24-h HRV and its four components were divided into 24 1-h segments. ApEn values were extracted and statistically analyzed. Four traditional HRV indices, namely standard deviation of the RR intervals, root mean square of successive differences, coefficient of variance of RR intervals and ratio of low to high power of HRV, were also calculated.ResultsThe low-frequency component contained the most abundant non-linear information, so was potentially most suitable for studying the cardiovascular system status with non-linear methods. ApEn values extracted from low- and high-frequency components of healthy controls were higher than those of diabetes patients. Except for root mean square of successive differences, standard deviation of the RR intervals, low to high power of HRV and coefficient of variance of RR intervals of healthy controls were all higher than those of diabetes patients.ConclusionsThe results showed that ApEn contained information on disorders of autonomic system function of diabetes patients as traditional HRV indices in time and frequency domains. ApEn and three traditional indices showed accordance to some degree. Non-linear information in subcomponents of HRV was shown, which is potentially more effective for distinguishing healthy individuals and diabetes patients than that extracted from the total HRV. Compared with diabetes patients, the cardiovascular system of healthy controls showed information of higher complexity, and better regulation function in response to changes of environment.

Heart Rate Dynamics after Combined Strength and Endurance Training in Middle-Aged Women: Heterogeneity of Responses

The loss of complexity in physiological systems may be a dynamical biomarker of aging and disease. In this study the effects of combined strength and endurance training compared with those of endurance training or strength training alone on heart rate (HR) complexity and traditional HR variability indices were examined in middle-aged women. 90 previously untrained female volunteers between the age of 40 and 65 years completed a 21 week progressive training period of either strength training, endurance training or their combination, or served as controls. Continuous HR time series were obtained during supine rest and submaximal steady state exercise. The complexity of HR dynamics was assessed using multiscale entropy analysis. In addition, standard time and frequency domain measures were also computed. Endurance training led to increases in HR complexity and selected time and frequency domain measures of HR variability (P<0.01) when measured during exercise. Combined strength and endurance training or strength training alone did not produce significant changes in HR dynamics. Inter-subject heterogeneity of responses was particularly noticeable in the combined training group. At supine rest, no training-induced changes in HR parameters were observed in any of the groups. The present findings emphasize the potential utility of endurance training in increasing the complex variability of HR in middle-aged women. Further studies are needed to explore the combined endurance and strength training adaptations and possible gender and age related factors, as well as other mechanisms, that may mediate the effects of different training regimens on HR dynamics.

Frailty and Impaired Cardiac Autonomic Control: New Insights From Principal Components Aggregation of Traditional Heart Rate Variability Indices

Age-related deterioration in homeostatic regulatory mechanisms leads to decreased complexity in their output. For example, the degradation of cardiac autonomic control results in loss of complexity in the heart rate signal. Frailty is a state of critically impaired homeostasis that results in heightened vulnerability to stressors. We propose a new measure of heart rate variability (HRV) to capture the impairment in cardiac autonomic control associated with frailty. Traditional time and frequency domain indices of HRV were obtained from 2-hour ambulatory electrocardiograms (ECGs) of 276 women (65-101 years old) in the Women's Health and Aging Study-I. Principal components analysis was conducted on the correlation matrix of HRV indices. Frailty was defined using a validated instrument. Regression models were used to evaluate associations of HRV measures with age, frailty, and 5-year mortality. The first two principal components (PCs), PC1 and PC2, explained 90% of the variance in HRV indices. PC1 is the mean of log-transformed HRV indices. PC2 is a linear combination of log-transformed indices, with positive weights for very low frequency (VLF), low frequency (LF), and standard deviation of N-N intervals (SDNN), and negative weights for high frequency (HF), root-mean-squared differences of successive N-N intervals (RMSSD), and proportion of all N-N intervals that are larger than 50 ms (pNN50). Decreases in SDNN, VLF, LF, and LF/HF were associated with an increased risk of frailty. PC2 was more strongly associated with age (beta = -.23, p < .001) and frailty (beta = -.73, p < 10(-5)) than were the individual HRV indices and LF/HF. PC2 was also the best predictor of 5-year mortality (beta = -.60, p < 10(-6)). Cardiac autonomic control, as reflected by HRV, is impaired in frailty. A new measure derived from PC aggregation of traditional HRV indices provides a compact summary of this impairment.

Circadian and Ultradian Rhythms in Cardiac Autonomic Modulation

The amount of time between heartbeats is controlled by the rate of depolarization of the sinoatrial node. This rate is controlled by a combination of sympathetic and parasympathetic inputs. For this reason, analysis of heart rate (HR) and heart rate variability (HRV) derived from intervals between normal heart beats provides insights into changing cardiac autonomic function. Increased sympathetic control of the heart from one time point to the next tends to increase HRs, decrease the amount of vagally modulated sinus arrhythmia measured as high-frequency (HF) spectral power, and increase the ratio between low-frequency (LF) spectral power and HF (the LF/HF ratio). Conversely, a shift toward greater vagal control of the heart tends to produce decreased HRs, increases in HF power, and decreases the LF/HF ratio. Clear circadian cycles of HR and HRV are seen in most subjects and correspond to periods of sleep and activity. The magnitude of these cycles is well captured by traditional HRV indices such as deviation of all normal-to-normal (NN) intervals. Cosinor analysis also provides a rough estimate of the magnitude of circadian rhythms. We have observed that HR and HRV fluctuate over time in a cyclic manner in most subjects. Although these cycles are especially prominent during the nighttime, they are seen in the daytime as well. We have developed a number of measures that quantify ultradian properties of HR/HRV cycles and would permit comparisons between subjects. We applied these measures during overnight polysomnography and have shown that ultradian rhythms of cardiac autonomic modulation have a variable and often weak correspondence with traditional sleep stages . In the current study, we applied the methodology developed for the nighttime recordings to 24-h recordings in young healthy subjects and compared these results to those obtained during the nighttime only in the same subjects and in an older group of cardiac patients. Our purpose was to explore the presenc

Sleep stage dependent patterns of nonlinear heart rate dynamics in postmenopausal women

Objective To study the effects of sleep stage changes on nocturnal nonlinear heart rate variability (HRV) in postmenopausal women. Design A prospective study. Population Seventy-one healthy postmenopausal women. Methods The women underwent two separate sleep studies four months apart. One steady state epoch per night of the awake state, stage 2 (light) non-REM sleep, stage 3–4 (deep) non-REM sleep and REM sleep were extracted. From the ECG, the fractal scaling exponents α 1 and α 2, approximate entropy (ApEn), the Poincaré plot variability coefficients SD1 and SD2, along with the low (LF) and high frequency (HF) bands of linear HRV as well as the LF/HF ratio were calculated. Results None of the spectral measures of HRV changed significantly during the non-REM sleep compared to awake state. However, in non-REM sleep, α 2 ( p < 0.001) decreased significantly compared to the awake state, while α 1 and ApEn remained unchanged. SD1 was slightly increased in stage 2 sleep ( p < 0.05), while SD2 decreased in slow wave sleep ( p < 0.001). In REM sleep, α 2 values returned to the awake values, while ApEn and α 1 increased above the awake levels ( p < 0.01 for all variables), and SD1 decreased ( p < 0.01). HF spectral component decreased slightly ( p < 0.05 compared to stage 2 sleep) and LF/HF ratio increased during REM sleep ( p < 0.001). ApEn and α 2 had no correlations with any of the spectral measures of HRV, and α 1 had a modest correlation with the LF/HF ratio only during sleep. Conclusions We found that nonlinear indices of HRV describe specific features in HR dynamics during various sleep stages that are not detected by traditional spectral HRV indices.