Heart rate dynamics before spontaneous onset of ventricular fibrillation in patients with healed myocardial infarcts

Characterization of autonomic states by complex sympathetic and parasympathetic dynamics

Assessment of heartbeat dynamics provides a promising framework for non-invasive monitoring of cardiovascular and autonomic states. Nevertheless, the non-specificity of such measurements among clinical populations and healthy conditions associated with different autonomic states severely limits their applicability and exploitation in naturalistic conditions. This limitation arises especially when pathological or postural change-related sympathetic hyperactivity is compared to autonomic changes across age and experimental conditions. In this frame, we investigate the intrinsic irregularity and complexity of cardiac sympathetic and vagal activity series in different populations, which are associated with different cardiac autonomic dynamics. Sample entropy, fuzzy entropy, and distribution entropy are calculated on the recently proposed sympathetic and parasympathetic activity indices (SAI and PAI) series, which are derived from publicly available heartbeat series of congestive heart failure patients, elderly and young subjects watching a movie in the supine position, and healthy subjects undergoing slow postural changes. Results show statistically significant differences between pathological/old subjects and young subjects in the resting state and during slow tilt, with interesting trends in SAI- and PAI-related entropy values. Moreover, while CHF patients and healthy subjects in upright position show the higher cardiac sympathetic activity, elderly and young subjects in resting state showed higher vagal activity. We conclude that quantification of intrinsic cardiac complexity from sympathetic and vagal dynamics may provide new physiology insights and improve on the non-specificity of heartbeat-derived biomarkers.

Prognostic value of heart rate variability in patients with coronary artery disease in the current treatment era

Coronary artery disease (CAD) mortality has declined substantially over the past decades thanks to advancing medical and interventional/surgical treatments; therefore, the prognostic value of the heart rate variability in CAD in the current treatment era is not well established. We evaluated the prognostic significance of baseline heart rate variability in 1,757 ARTEMIS study patients with angiographically verified CAD. During an average follow-up time of 8.7 ± 2.2 years, a total of 285 (16.2%) patients died. Of the patients, 63 (3.6%) suffered sudden cardiac death or were resuscitated from sudden cardiac arrest (SCD/SCA), 60 (3.4%) experienced non-sudden cardiac death (NSCD), and death attributable to non-cardiac causes (NCD) occurred in 162 (9.2%) patients. For every 10 ms decrease in standard deviation of normal to normal intervals the risk for SCD/SCA, NSCD and NCD increased significantly: HR 1.153 (95% CI 1.075–1.236, p<0.001), HR 1.187 (95% CI 1.102–1.278, p<0.001) and HR 1.080 (95% CI 1.037–1.125, p<0.001), respectively. The natural logarithm of the low-frequency component of the power spectrum and the short-term scaling exponent of the detrended fluctuation analysis also had significant association with all modes of death (p<0.001). After relevant adjustment, standard deviation of normal-to-normal intervals retained its association with NSCD and NCD (p<0.01), the natural logarithm of the low-frequency component of the power spectrum with all modes of death (p from <0.05 to <0.01), and the short-term scaling exponent of the detrended fluctuation analysis with SCD/SCA (p<0.05) and NCD (p<0.001). In conclusion, impairment of many measures of heart rate variability predicts mortality but is not associated with any specific mode of death in patients with stable CAD during the current treatment era, limiting the clinical applicability of heart rate variability to targeting therapy.

Intrinsic Complexity of Sympathetic and Parasympathetic Dynamics from HRV series: a Preliminary Study on Postural Changes

The analysis of complex heartbeat dynamics has been widely used to characterize heartbeat autonomic control in healthy and pathological conditions. However, underlying physiological correlates of complexity measurements from heart rate variability (HRV) series have not been identified yet. To this extent, we investigated intrinsic irregularity and complexity of cardiac sympathetic and vagal activity time series during postural changes. We exploited our recently proposed HRV-based, time-varying Sympathetic and Parasympathetic Activity Indices (SAI and PAI) and performed Sample Entropy, Fuzzy Entropy, and Distribution Entropy calculations on publicly-available heartbeat series gathered from 10 healthy subjects undergoing resting state and passive slow tilt sessions. Results show significantly higher entropy values during the upright position than resting state in both SAI and PAI series. We conclude that an increase in HRV complexity resulting from postural changes may derive from sympathetic and vagal activities with higher complex dynamics.

Complexity-Based Measures of Heart Rate Dynamics in Older Adults Following Long- and Short-Term Tai Chi Training: Cross-sectional and Randomized Trial Studies

Measures characterizing the complexity of heart rate (HR) dynamics have been informative in predicting age- and disease-related decline in cardiovascular health, but few studies have evaluated whether mind-body exercise can impact HR complexity. This study evaluated the effects of long-term Tai Chi (TC) practice on the complexity of HR dynamics using an observational comparison of TC experts and age- and gender-matched TC-naïve individuals. Shorter-term effects of TC were assessed by randomly assigning TC-naïve participants to either TC group to receive six months of TC training or to a waitlist control group. 23 TC experts (age = 63.3 ± 8.0 y; 24.6 ± 12.0 y TC experience) and 52 TC-naïve (age = 64.3 ± 7.7 y) were enrolled. In cross-sectional analyses, TC experts had a higher overall complexity index (CI, p = 0.004) and higher entropy at multiple individual time scales (p < 0.05); these findings persisted in models accounting for age, gender, body mass index (BMI), and physical activity levels. Longitudinal changes in complexity index did not differ significantly following random assignment to six months of TC vs. a waitlist control; however, within the TC group, complexity at select time scales showed statistically non-significant trends toward increases. Our study supports that longer-term TC mind-body training may be associated with increased complexity of HR dynamics.

Scaling and correlation properties of RR and QT intervals at the cellular level

We study complex scaling properties of RR and QT intervals of electrocardiograms (ECGs) with their equivalences at the cellular level, that is, inter-beat intervals (IBI) and field potential durations (FPD) of spontaneously beating human-induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) aggregates. Our detrended fluctuation analysis and Poincaré plots reveal remarkable similarities between the ECG and hiPSC-CM data. In particular, no statistically significant difference was found in the short- and long-term scaling exponents α₁ and α₂ of RR and QT intervals and their cellular equivalences. Previously unknown scaling properties of FPDs of hiPSC-CM aggregates reveal that the increasing scaling exponent of QT intervals as a function of the time scale, is an intrinsic feature at the cellular level.

Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data

Implantable-cardioverter defibrillators (ICD) detect and terminate life-threatening ventricular tachyarrhythmia with electric shocks after they occur. This puts patients at risk if they are driving or in a situation where they can fall. ICD's shocks are also very painful and affect a patient's quality of life. It would be ideal if ICDs can accurately predict the occurrence of ventricular tachyarrhythmia and then issue a warning or provide preventive therapy. Our study explores the use of ICD data to automatically predict ventricular arrhythmia using heart rate variability (HRV). A 5 minute and a 10 second warning system are both developed and compared. The participants for this study consist of 788 patients who were enrolled in the ICD arm of the Sudden Cardiac Death-Heart Failure Trial (SCD-HeFT). Two groups of patient rhythms, regular heart rhythms and pre-ventricular-tachyarrhythmic rhythms, are analyzed and different HRV features are extracted. Machine learning algorithms, including random forests (RF) and support vector machines (SVM), are trained on these features to classify the two groups of rhythms in a subset of the data comprising the training set. These algorithms are then used to classify rhythms in a separate test set. This performance is quantified by the area under the curve (AUC) of the ROC curve. Both RF and SVM methods achieve a mean AUC of 0.81 for 5-minute prediction and mean AUC of 0.87-0.88 for 10-second prediction; an AUC over 0.8 typically warrants further clinical investigation. Our work shows that moderate classification accuracy can be achieved to predict ventricular tachyarrhythmia with machine learning algorithms using HRV features from ICD data. These results provide a realistic view of the practical challenges facing implementation of machine learning algorithms to predict ventricular tachyarrhythmia using HRV data, motivating continued research on improved algorithms and additional features with higher predictive power.

A clinical approach to arrhythmias revisited in 2018 : From ECG over noninvasive and invasive electrophysiology to advanced imaging

Understanding arrhythmias and their treatment is not always easy. The current straightforward approach with catheter ablation and device therapy is an amazing achievement, but does not make management of underlying or other cardiac disease and pharmacological therapy unnecessary. The goal of this paper is to describe how much of the knowledge of the 1980s and early 1990s can and should still be applied in the modern treatment of patients with arrhythmias. After an introduction, this review will focus on paroxysmal atrial fibrillation and a prototype of ‘idiopathic’ ventricular arrhythmias, two diseases with a striking similarity, and will discuss the arrhythmogenesis. The ECG continues to play an important role in diagnostics. Both diseases are associated with a structurally normal heart; the autonomic nervous system plays an important role in triggering arrhythmias at both the atrial and ventricular level.

Heart Rate Fragmentation: A Symbolic Dynamical Approach

Background: We recently introduced the concept of heart rate fragmentation along with a set of metrics for its quantification. The term was coined to refer to an increase in the percentage of changes in heart rate acceleration sign, a dynamical marker of a type of anomalous variability. The effort was motivated by the observation that fragmentation, which is consistent with the breakdown of the neuroautonomic-electrophysiologic control system of the sino-atrial node, could confound traditional short-term analysis of heart rate variability.

Objective: The objectives of this study were to: (1) introduce a symbolic dynamical approach to the problem of quantifying heart rate fragmentation; (2) evaluate how the distribution of the different dynamical patterns (“words”) varied with the participants' age in a group of healthy subjects and patients with coronary artery disease (CAD); and (3) quantify the differences in the fragmentation patterns between the two sample populations.

Methods: The symbolic dynamical method employed here was based on a ternary map of the increment NN interval time series and on the analysis of the relative frequency of symbolic sequences (words) with a pre-defined set of features. We analyzed annotated, open-access Holter databases of healthy subjects and patients with CAD, provided by the University of Rochester Telemetric and Holter ECG Warehouse (THEW).

Results: The degree of fragmentation was significantly higher in older individuals than in their younger counterparts. However, the fragmentation patterns were different in the two sample populations. In healthy subjects, older age was significantly associated with a higher percentage of transitions from acceleration/deceleration to zero acceleration and vice versa (termed “soft” inflection points). In patients with CAD, older age was also significantly associated with higher percentages of frank reversals in heart rate acceleration (transitions from acceleration to deceleration and vice versa, termed “hard” inflection points). Compared to healthy subjects, patients with CAD had significantly higher percentages of soft and hard inflection points, an increased percentage of words with a high degree of fragmentation and a decreased percentage of words with a lower degree of fragmentation.

Conclusion: The symbolic dynamical method employed here was useful to probe the newly recognized property of heart rate fragmentation. The findings from these cross-sectional studies confirm that CAD and older age are associated with higher levels of heart rate fragmentation. Furthermore, fragmentation with healthy aging appears to be phenotypically different from fragmentation in the context of CAD.

The Effects of Pharmacological Compounds on Beat Rate Variations in Human Long QT-Syndrome Cardiomyocytes

Healthy human heart rate fluctuates overtime showing long-range fractal correlations. In contrast, various cardiac diseases and normal aging show the breakdown of fractal complexity. Recently, it was shown that human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) intrinsically exhibit fractal behavior as in humans. Here, we investigated the fractal complexity of hiPSC-derived long QT-cardiomyocytes (LQT-CMs). We recorded extracellular field potentials from hiPSC-CMs at baseline and under the effect of various compounds including β-blocker bisoprolol, ML277, a specific and potent I_Ks current activator, as well as JNJ303, a specific I_Ks blocker. From the peak-to-peak-intervals, we determined the long-range fractal correlations by using detrended fluctuation analysis. Electrophysiologically, the baseline corrected field potential durations (cFPDs) were more prolonged in LQT-CMs than in wildtype (WT)-CMs. Bisoprolol did not have significant effects to the cFPD in any CMs. ML277 shortened cFPD in a dose-dependent fashion by 11 % and 5–11 % in WT- and LQT-CMs, respectively. JNJ303 prolonged cFPD in a dose-dependent fashion by 22 % and 7–13 % in WT- and LQT-CMs, respectively. At baseline, all CMs showed fractal correlations as determined by short-term scaling exponent α. However, in all CMs, the α was increased when pharmacological compounds were applied indicating of breakdown of fractal complexity. These findings suggest that the intrinsic mechanisms contributing to the fractal complexity are not altered in LQT-CMs. The modulation of I_Ks channel and β1-adrenoreceptors by pharmacological compounds may affect the fractal complexity of the hiPSC-CMs.

Impaired cardiac autonomic regulation and long-term risk of atrial fibrillation in patients with coronary artery disease

BACKGROUND

The influence of autonomic cardiac regulation on long-term risk of new-onset atrial fibrillation (AF) in coronary artery disease (CAD) is not well established.

OBJECTIVE

The purpose of this study was to evaluate the value of heart rate variability, a marker of cardiac autonomic regulation, in predicting new-onset AF in CAD.

METHODS

The Innovation to Reduce Cardiovascular Complications of Diabetes at the Intersection study population consisted of 1946 patients with CAD. After exclusions, the present analysis included 1710 patients. Those patients had a 24-hour electrocardiographic recording at baseline.

RESULTS

A total of 143 cases (8.4%) of new-onset AF were observed during a follow-up of 5.6 ± 1.5 years. The lower values of the short-term scaling exponent of the detrended fluctuation analysis (DFA1) and the ratio of the low-frequency and high-frequency components of the power spectrum (LF/HF ratio) remained the strongest heart rate variability predictors of the development of AF after relevant adjustments in Cox multivariate regression analysis (P < .001 for both). The accuracy of these parameters in prediction of AF was even better (area under the receiver operating characteristic curve 0.630 and 0.631, respectively) than that of echocardiographic left atrial diameter (area under the curve 0.618). Including DFA1 and LF/HF ratio in the AF risk model increased the C-index from 0.630 (95% confidence interval 0.569-0.692) to 0.666 (95% confidence interval 0.612-0.720).

CONCLUSION

Impaired cardiac autonomic regulation measured by DFA1 and LF/HF ratio predicts the development of new-onset AF as well as or even better than left atrial diameter in patients with CAD.

Cardiac Autonomic Responses during Exercise and Post-exercise Recovery Using Heart Rate Variability and Systolic Time Intervals-A Review

Cardiac parasympathetic activity may be non-invasively investigated using heart rate variability (HRV), although HRV is not widely accepted to reflect sympathetic activity. Instead, cardiac sympathetic activity may be investigated using systolic time intervals (STI), such as the pre-ejection period. Although these autonomic indices are typically measured during rest, the “reactivity hypothesis” suggests that investigating responses to a stressor (e.g., exercise) may be a valuable monitoring approach in clinical and high-performance settings. However, when interpreting these indices it is important to consider how the exercise dose itself (i.e., intensity, duration, and modality) may influence the response. Therefore, the purpose of this investigation was to review the literature regarding how the exercise dosage influences these autonomic indices during exercise and acute post-exercise recovery. There are substantial methodological variations throughout the literature regarding HRV responses to exercise, in terms of exercise protocols and HRV analysis techniques. Exercise intensity is the primary factor influencing HRV, with a greater intensity eliciting a lower HRV during exercise up to moderate-high intensity, with minimal change observed as intensity is increased further. Post-exercise, a greater preceding intensity is associated with a slower HRV recovery, although the dose-response remains unclear. A longer exercise duration has been reported to elicit a lower HRV only during low-moderate intensity and when accompanied by cardiovascular drift, while a small number of studies have reported conflicting results regarding whether a longer duration delays HRV recovery. “Modality” has been defined multiple ways, with limited evidence suggesting exercise of a greater muscle mass and/or energy expenditure may delay HRV recovery. STI responses during exercise and recovery have seldom been reported, although limited data suggests that intensity is a key determining factor. Concurrent monitoring of HRV and STI may be a valuable non-invasive approach to investigate autonomic stress reactivity; however, this integrative approach has not yet been applied with regards to exercise stressors.

Potential effects of intrinsic heart pacemaker cell mechanisms on dysrhythmic cardiac action potential firing

The heart's regular electrical activity is initiated by specialized cardiac pacemaker cells residing in the sinoatrial node. The rate and rhythm of spontaneous action potential firing of sinoatrial node cells are regulated by stochastic mechanisms that determine the level of coupling of chemical to electrical clocks within cardiac pacemaker cells. This coupled-clock system is modulated by autonomic signaling from the brain via neurotransmitter release from the vagus and sympathetic nerves. Abnormalities in brain-heart clock connections or in any molecular clock activity within pacemaker cells lead to abnormalities in the beating rate and rhythm of the pacemaker tissue that initiates the cardiac impulse. Dysfunction of pacemaker tissue can lead to tachy-brady heart rate alternation or exit block that leads to long atrial pauses and increases susceptibility to other cardiac arrhythmia. Here we review evidence for the idea that disturbances in the intrinsic components of pacemaker cells may be implemented in arrhythmia induction in the heart.

Paradoxical response to an emotional task: trait characteristics and heart-rate dynamics

The present study evaluated the heart-rate dynamics of subjects reporting decreased (responders) or paradoxically increased relaxation (nonresponders) at the end of a threatening movie. Heart-rate dynamics were characterized by indices extracted through recurrence quantification analysis (RQA) and detrended fluctuation analysis (DFA). These indices were studied as a function of a few individual characteristics: hypnotizability, gender, absorption, anxiety, and the activity of the behavioral inhibition and activation systems (BIS/BAS). Results showed that (a) the subjective experience of responsiveness is associated with the activity of the behavioral inhibition system and (b) a few RQA and DFA indices are able to capture the influence of cognitive-emotional traits, including hypnotizability, on the responsiveness to the threatening task.

Morning surge of ventricular arrhythmias in a new arrhythmogenic canine model of chronic heart failure is associated with attenuation of time-of-day dependence of heart rate and autonomic adaptation, and reduced cardiac chaos

Patients with chronic heart failure (CHF) exhibit a morning surge in ventricular arrhythmias, but the underlying cause remains unknown. The aim of this study was to determine if heart rate dynamics, autonomic input (assessed by heart rate variability (HRV)) and nonlinear dynamics as well as their abnormal time-of-day-dependent oscillations in a newly developed arrhythmogenic canine heart failure model are associated with a morning surge in ventricular arrhythmias. CHF was induced in dogs by aortic insufficiency & aortic constriction, and assessed by echocardiography. Holter monitoring was performed to study time-of-day-dependent variation in ventricular arrhythmias (PVCs, VT), traditional HRV measures, and nonlinear dynamics (including detrended fluctuations analysis α1 and α2 (DFAα1 & DFAα2), correlation dimension (CD), and Shannon entropy (SE)) at baseline, as well as 240 days (240 d) and 720 days (720 d) following CHF induction. LV fractional shortening was decreased at both 240 d and 720 d. Both PVCs and VT increased with CHF duration and showed a morning rise (2.5-fold & 1.8-fold increase at 6 AM-noon vs midnight-6 AM) during CHF. The morning rise in HR at baseline was significantly attenuated by 52% with development of CHF (at both 240 d & 720 d). Morning rise in the ratio of low frequency to high frequency (LF/HF) HRV at baseline was markedly attenuated with CHF. DFAα1, DFAα2, CD and SE all decreased with CHF by 31, 17, 34 and 7%, respectively. Time-of-day-dependent variations in LF/HF, CD, DFA α1 and SE, observed at baseline, were lost during CHF. Thus in this new arrhythmogenic canine CHF model, attenuated morning HR rise, blunted autonomic oscillation, decreased cardiac chaos and complexity of heart rate, as well as aberrant time-of-day-dependent variations in many of these parameters were associated with a morning surge of ventricular arrhythmias.

Physiologic complexity and aging: implications for physical function and rehabilitation

The dynamics of most healthy physiological processes are complex, in that they are comprised of fluctuations with information-rich structure correlated over multiple temporospatial scales. Lipsitz and Goldberger (1992) first proposed that the aging process may be characterized by a progressive loss of physiologic complexity. We contend that this loss of complexity results in functional decline of the organism by diminishing the range of available, adaptive responses to the innumerable stressors of everyday life. From this relationship, it follows that rehabilitative interventions may be optimized by targeting the complex dynamics of human physiology, and by quantifying their effects using tools derived from complex systems theory. Here, we first discuss several caveats that one must consider when examining the functional and rehabilitative implications of physiologic complexity. We then review available evidence regarding the relationship between physiologic complexity and system functionality, as well as the potential for interventions to restore the complex dynamics that characterize healthy physiological function.

Two-dimensional matrix algorithm using detrended fluctuation analysis to distinguish Burkitt and diffuse large B-cell lymphoma

A detrended fluctuation analysis (DFA) method is applied to image analysis. The 2-dimensional (2D) DFA algorithms is proposed for recharacterizing images of lymph sections. Due to Burkitt lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL), there is a significant different 5-year survival rates after multiagent chemotherapy. Therefore, distinguishing the difference between BL and DLBCL is very important. In this study, eighteen BL images were classified as group A, which have one to five cytogenetic changes. Ten BL images were classified as group B, which have more than five cytogenetic changes. Both groups A and B BLs are aggressive lymphomas, which grow very fast and require more intensive chemotherapy. Finally, ten DLBCL images were classified as group C. The short-term correlation exponent α1 values of DFA of groups A, B, and C were 0.370 ± 0.033, 0.382 ± 0.022, and 0.435 ± 0.053, respectively. It was found that α1 value of BL image was significantly lower (P < 0.05) than DLBCL. However, there is no difference between the groups A and B BLs. Hence, it can be concluded that α1 value based on DFA statistics concept can clearly distinguish BL and DLBCL image.

DETRENDED FLUCTUATION ANALYSES OF SHORT-TERM HEART RATE VARIABILITY IN SURGICAL INTENSIVE CARE UNITS

We examine the dynamics of complex physiologic fluctuations using methods developed very recently in statistical physics. The method based on detrended fluctuation analysis (DFA) has been used to investigate the profile of different types of physiologic states under long term (i.e., 24 hr) analysis of heart rate variability (HRV). In this paper, this method to investigate the output of central physiologic control system under short term (i.e., 1 hr) of HRV in surgical intensive care units (SICU). Electrocardiograph (ECG) signals lasting around 1 hr were collected from ten college student volunteers as group A. Ten computes-generates white noise signals as group B also provided ECG signals lasting around 1 hr. Finally, seventeen patients representing 37 cases undergoing different types of neurosurgery were studied as group C. From this group, 25 cases were selected from 15 patients with brain injury and 12 cases were selected from 2 patients with septicemia. Group A and B were used as high and low limits of baseline for comparison with pathologic states in the SICU. The a values of DFA of groups A, B, and C were 0.958 ± 0.034, 0.521 ± 0.010, and 0.815 ± 0.183, respectively. It was found that the α value of patients in the SICU was significantly lower (P < 0.05) than that of healthy volunteers and significantly higher (P < 0.05) than white noise signals. Hence, it can be concluded that α values based on the DFA statistical concept can clearly distinguish pathologic states in SICU patients from the healthy group and from white noise signals.

Isometric handgrip training lowers blood pressure and increases heart rate complexity in medicated hypertensive patients

Hypertension is characterized by elevated blood pressure (BP) and autonomic dysfunction, both thought to be improved with exercise training. Isometric handgrip (IHG) training may represent a beneficial, time-effective exercise therapy. We investigated the effects of IHG training on BP and traditional and nonlinear measures of heart rate variability (HRV). Pre- and post-measurements of BP and HRV were determined in 23 medicated hypertensive participants (mean ± SEM, 66 ± 2 years) following either 8 weeks of IHG training (n = 13) or control (n = 10). IHG exercise consisted of four unilateral 2-min isometric contractions at 30% of maximal voluntary contraction, each separated by 4 min of rest. IHG training was performed 3 days/week for 8 weeks. IHG training decreased systolic BP (125 ± 3 mmHg to 120 ± 2 mmHg, P < 0.05) and mean BP (90 ± 2 mmHg to 87 ± 2 mmHg, P < 0.05), while sample entropy was increased (1.07 ± 0.1 to 1.35 ± 0.1, P < 0.05) and the fractal scaling distance score was decreased (0.34 ± 0.1 to 0.19 ± 0.1, P < 0.05). No significant changes were observed in traditional spectral or time-domain measures of HRV or control participants. IHG training improves nonlinear HRV, but not traditional HRV, while reducing systolic and mean BP. These results may highlight the benefits of IHG training for patients with primary hypertension.

Origin of heart rate variability and turbulence: an appraisal of autonomic modulation of cardiovascular function

Heart period constantly changes on a beat to beat basis, due to autonomic influences on the sinoatrial node, and changes can be quantified as heart rate variability (HRV). In addition, after a premature ventricular beat, there are reproducible variations in RR interval, also due to baroreflex mediated autonomic influences on the sinoatrial node, that can be measured as heart rate turbulence (HRT). Impaired autonomic function as measured by HRV and HRT has proven to predict adverse outcomes in clinical settings. The ability of reduced HRV and HRT to predict adverse outcomes has been explained by their dependency on vagal mechanisms that could reflect an increased sympathetic and a reduced vagal modulation of sinus node, thus favoring cardiac electrical instability. Analysis of non-linear dynamics of HRV has also been utilized to describe the fractal like characteristic of the variability signal and proven effective in identify patients at risk for sudden cardiac death. Despite the clinical validity of these measures, it has also been evident that the relationship between neural input and sinus node responsiveness is extremely complex and variable in different clinical conditions. Thus, abnormal HRV or HRT on a clinical Holter recordings may reflect non-neural as well as autonomic mechanisms, and this also needs to be taken into account when interpreting any findings. However, under controlled conditions, the computation of the low and high frequency components of HRV and of their normalized powers or ratio seems capable of providing valid information on sympatho-vagal balance in normal subjects, as well as in most patients with a preserved left ventricular function. Thus, analysis of HRV does provide a unique tool to specifically assess autonomic control mechanisms in association with various perturbations. In conclusion, HRV measures are of substantial utility to identify patients with an increased cardiac mortality and to evaluate autonomic control mechanisms, but their ability to capture specific levels of autonomic control may be limited to controlled laboratory studies in relatively healthy subjects.

Heart rate variability - a historical perspective

Heart rate variability (HRV), the beat-to-beat variation in either heart rate or the duration of the R-R interval - the heart period, has become a popular clinical and investigational tool. The temporal fluctuations in heart rate exhibit a marked synchrony with respiration (increasing during inspiration and decreasing during expiration - the so called respiratory sinus arrhythmia, RSA) and are widely believed to reflect changes in cardiac autonomic regulation. Although the exact contributions of the parasympathetic and the sympathetic divisions of the autonomic nervous system to this variability are controversial and remain the subject of active investigation and debate, a number of time and frequency domain techniques have been developed to provide insight into cardiac autonomic regulation in both health and disease. It is the purpose of this essay to provide an historical overview of the evolution in the concept of HRV. Briefly, pulse rate was first measured by ancient Greek physicians and scientists. However, it was not until the invention of the "Physician's Pulse Watch" (a watch with a second hand that could be stopped) in 1707 that changes in pulse rate could be accurately assessed. The Rev. Stephen Hales (1733) was the first to note that pulse varied with respiration and in 1847 Carl Ludwig was the first to record RSA. With the measurement of the ECG (1895) and advent of digital signal processing techniques in the 1960s, investigation of HRV and its relationship to health and disease has exploded. This essay will conclude with a brief description of time domain, frequency domain, and non-linear dynamic analysis techniques (and their limitations) that are commonly used to measure HRV.

Heart rate variability and non-linear dynamics in risk stratification

The time-domain measures and power–spectral analysis of heart rate variability (HRV) are classic conventional methods to assess the complex regulatory system between autonomic nervous system and heart rate and are most widely used. There are abundant scientific data about the prognostic significance of the conventional measurements of HRV in patients with various conditions, particularly with myocardial infarction. Some studies have suggested that some newer measures describing non-linear dynamics of heart rate, such as fractal measures, may reveal prognostic information beyond that obtained by the conventional measures of HRV. An ideal risk indicator could specifically predict sudden arrhythmic death as the implantable cardioverter-defibrillator (ICD) therapy can prevent such events. There are numerically more sudden deaths among post-infarction patients with better preserved left ventricular function than in those with severe left ventricular dysfunction. Recent data support the concept that HRV measurements, when analyzed several weeks after acute myocardial infarction, predict life-threatening ventricular tachyarrhythmias in patients with moderately depressed left ventricular function. However, well-designed prospective randomized studies are needed to evaluate whether the ICD therapy based on the assessment of HRV alone or with other risk indicators improves the patients’ prognosis. Several issues, such as the optimal target population, optimal timing of HRV measurements, optimal methods of HRV analysis, and optimal cutpoints for different HRV parameters, need clarification before the HRV analysis can be a widespread clinical tool in risk stratification.

Breakdown of the intermediate-term fractal scaling exponent in sinus node dysfunction. New method for non-invasive evaluation of sinus node function

BACKGROUND

The aim of the present study was to characterize the heart rate dynamics of sinus bradycardia (SB) from sinus node dysfunction (SND) using non-linear dynamical system analysis. No data are yet available on how the dynamics change in the presence of SND.

METHODS AND RESULTS

Conventional time and frequency domain analysis, the short- (DFAα(1)) and intermediate-term fractal scaling exponent (DFAα(2)), approximate entropy (ApEn) and sample entropy (SampEn) were calculated in 60-min sinus RR interval data of SB from 24-h ambulatory electrocardiograms of 110 patients: 44 SND patients, 44 age-matched controls, and 22 younger controls. All of the time and frequency domain parameters, ApEn and SampEn, were significantly reduced in the age-matched control group, compared with the young control group. DFAα(1) and DFAα(2) increased with aging. Both the DFAα(1) and DFAα(2) of SND patients were paradoxically reduced, which was not appropriate for their age. Only the percentage of consecutive RR intervals with absolute differences >50ms (pNN(50)), low-frequency power, and DFAα(2) made a significant contribution to prediction of SND on logistic regression analysis. Among them, DFAα(2) was the most significant variable for prediction of SND (odds ratio, 0.927; 95% confidence interval: 0.888-0.969, P=0.001). DFAα(2) remained as a significant variable for prediction of SND, when compared with overall control patients, combining the 2 control groups.

CONCLUSIONS

Inappropriate reduction of DFAα(2) is a robust measure and could be an adjunctive tool for improvement of diagnostic performance in detection of SND.

Heart rate variability and non-linear dynamics in risk stratification

The time-domain measures and power-spectral analysis of heart rate variability (HRV) are classic conventional methods to assess the complex regulatory system between autonomic nervous system and heart rate and are most widely used. There are abundant scientific data about the prognostic significance of the conventional measurements of HRV in patients with various conditions, particularly with myocardial infarction. Some studies have suggested that some newer measures describing non-linear dynamics of heart rate, such as fractal measures, may reveal prognostic information beyond that obtained by the conventional measures of HRV. An ideal risk indicator could specifically predict sudden arrhythmic death as the implantable cardioverter-defibrillator (ICD) therapy can prevent such events. There are numerically more sudden deaths among post-infarction patients with better preserved left ventricular function than in those with severe left ventricular dysfunction. Recent data support the concept that HRV measurements, when analyzed several weeks after acute myocardial infarction, predict life-threatening ventricular tachyarrhythmias in patients with moderately depressed left ventricular function. However, well-designed prospective randomized studies are needed to evaluate whether the ICD therapy based on the assessment of HRV alone or with other risk indicators improves the patients' prognosis. Several issues, such as the optimal target population, optimal timing of HRV measurements, optimal methods of HRV analysis, and optimal cutpoints for different HRV parameters, need clarification before the HRV analysis can be a widespread clinical tool in risk stratification.

Heart rate variability - a historical perspective

Heart rate variability (HRV), the beat-to-beat variation in either heart rate or the duration of the R-R interval - the heart period, has become a popular clinical and investigational tool. The temporal fluctuations in heart rate exhibit a marked synchrony with respiration (increasing during inspiration and decreasing during expiration - the so called respiratory sinus arrhythmia, RSA) and are widely believed to reflect changes in cardiac autonomic regulation. Although the exact contributions of the parasympathetic and the sympathetic divisions of the autonomic nervous system to this variability are controversial and remain the subject of active investigation and debate, a number of time and frequency domain techniques have been developed to provide insight into cardiac autonomic regulation in both health and disease. It is the purpose of this essay to provide an historical overview of the evolution in the concept of HRV. Briefly, pulse rate was first measured by ancient Greek physicians and scientists. However, it was not until the invention of the "Physician's Pulse Watch" (a watch with a second hand that could be stopped) in 1707 that changes in pulse rate could be accurately assessed. The Rev. Stephen Hales (1733) was the first to note that pulse varied with respiration and in 1847 Carl Ludwig was the first to record RSA. With the measurement of the ECG (1895) and advent of digital signal processing techniques in the 1960s, investigation of HRV and its relationship to health and disease has exploded. This essay will conclude with a brief description of time domain, frequency domain, and non-linear dynamic analysis techniques (and their limitations) that are commonly used to measure HRV.

Fractals for physicians

There is increasing interest in the study of fractals in medicine. In this review, we provide an overview of fractals, of techniques available to describe fractals in physiological data, and we propose some reasons why a physician might benefit from an understanding of fractals and fractal analysis, with an emphasis on paediatric respiratory medicine where possible. Among these reasons are the ubiquity of fractal organisation in nature and in the body, and how changes in this organisation over the lifespan provide insight into development and senescence. Fractal properties have also been shown to be altered in disease and even to predict the risk of worsening of disease. Finally, implications of a fractal organisation include robustness to errors during development, ability to adapt to surroundings, and the restoration of such organisation as targets for intervention and treatment.

Effect of extreme data loss on long-range correlated and anticorrelated signals quantified by detrended fluctuation analysis

Detrended fluctuation analysis (DFA) is an improved method of classical fluctuation analysis for nonstationary signals where embedded polynomial trends mask the intrinsic correlation properties of the fluctuations. To better identify the intrinsic correlation properties of real-world signals where a large amount of data is missing or removed due to artifacts, we investigate how extreme data loss affects the scaling behavior of long-range power-law correlated and anticorrelated signals. We introduce a segmentation approach to generate surrogate signals by randomly removing data segments from stationary signals with different types of long-range correlations. The surrogate signals we generate are characterized by four parameters: (i) the DFA scaling exponent alpha of the original correlated signal u(i) , (ii) the percentage p of the data removed from u(i) , (iii) the average length mu of the removed (or remaining) data segments, and (iv) the functional form P(l) of the distribution of the length l of the removed (or remaining) data segments. We find that the global scaling exponent of positively correlated signals remains practically unchanged even for extreme data loss of up to 90%. In contrast, the global scaling of anticorrelated signals changes to uncorrelated behavior even when a very small fraction of the data is lost. These observations are confirmed on two examples of real-world signals: human gait and commodity price fluctuations. We further systematically study the local scaling behavior of surrogate signals with missing data to reveal subtle deviations across scales. We find that for anticorrelated signals even 10% of data loss leads to significant monotonic deviations in the local scaling at large scales from the original anticorrelated to uncorrelated behavior. In contrast, positively correlated signals show no observable changes in the local scaling for up to 65% of data loss, while for larger percentage of data loss, the local scaling shows overestimated regions (with higher local exponent) at small scales, followed by underestimated regions (with lower local exponent) at large scales. Finally, we investigate how the scaling is affected by the average length, probability distribution, and percentage of the remaining data segments in comparison to the removed segments. We find that the average length mu_{r} of the remaining segments is the key parameter which determines the scales at which the local scaling exponent has a maximum deviation from its original value. Interestingly, the scales where the maximum deviation occurs follow a power-law relationship with mu_{r} . Whereas the percentage of data loss determines the extent of the deviation. The results presented in this paper are useful to correctly interpret the scaling properties obtained from signals with extreme data loss.

Reduced physiological complexity in robust elderly adults with the APOE epsilon4 allele

BACKGROUND

It is unclear whether the loss of physiological complexity during the aging process is due to genetic variations. The APOE gene has been studied extensively in regard to its relationship with aging-associated medical illness. We hypothesize that diminished physiological complexity, as measured by heart rate variability, is influenced by polymorphisms in the APOE allele among elderly individuals.

METHODOLOGY/PRINCIPAL FINDINGS

A total of 102 robust, non-demented, elderly subjects with normal functions of daily activities participated in this study (97 males and 5 females, aged 79.2+/-4.4 years, range 72-92 years). Among these individuals, the following two APOE genotypes were represented: epsilon4 non-carriers (n = 87, 85.3%) and epsilon4 carriers (n = 15, 14.7%). Multi-scale entropy (MSE), an analysis used in quantifying complexity for nonlinear time series, was employed to analyze heart-rate dynamics. Reduced physiological complexity, as measured by MSE, was significantly associated with the presence of the APOE epsilon4 allele in healthy elderly subjects, as compared to APOE epsilon4 allele non-carriers (24.6+/-5.5 versus 28.9+/-5.2, F = 9.429, p = 0.003, respectively).

CONCLUSIONS/SIGNIFICANCE

This finding suggests a role for the APOE gene in the diminished physiological complexity seen in elderly populations.

Deterministic chaos and fractal complexity in the dynamics of cardiovascular behavior: perspectives on a new frontier

Physiological systems such as the cardiovascular system are capable of five kinds of behavior: equilibrium, periodicity, quasi-periodicity, deterministic chaos and random behavior. Systems adopt one or more these behaviors depending on the function they have evolved to perform. The emerging mathematical concepts of fractal mathematics and chaos theory are extending our ability to study physiological behavior. Fractal geometry is observed in the physical structure of pathways, networks and macroscopic structures such the vasculature and the His-Purkinje network of the heart. Fractal structure is also observed in processes in time, such as heart rate variability. Chaos theory describes the underlying dynamics of the system, and chaotic behavior is also observed at many levels, from effector molecules in the cell to heart function and blood pressure. This review discusses the role of fractal structure and chaos in the cardiovascular system at the level of the heart and blood vessels, and at the cellular level. Key functional consequences of these phenomena are highlighted, and a perspective provided on the possible evolutionary origins of chaotic behavior and fractal structure. The discussion is non-mathematical with an emphasis on the key underlying concepts.

Local scale exponents of blood pressure and heart rate variability by detrended fluctuation analysis: effects of posture, exercise, and aging

Heart rate self-affinity is often assessed by detrended fluctuations analysis, obtaining two coefficients only: a short-term (alpha(1)) exponent and a long-term (alpha(2)) exponent. Our aim is to show the limits of this approach and alternatively propose the estimation of the whole spectrum of local exponents alpha(n) for heart rate and blood pressure. To illustrate the advantages of this approach, we assess the effects of autonomic activations and age on alpha(n). We measured ECG and arterial pressure in 60 volunteers for 10 min, considering three conditions at increasing sympathetic activation: supine rest, sitting, and sitting during exercise. We computed alpha(n) of R-R intervals and systolic, mean, and diastolic blood pressures, as the slope of the detrended fluctuations function in a log-log plot. Volunteers were divided into age groups and compared. Results indicate that: 1) alpha(1) cannot be defined because short-term coefficients decrease with n, while alpha(2) cannot be defined only for blood pressure during supine rest; 2) heart rate and blood pressure scaling structures differ during supine rest but not during exercise; and 3) age effects appear mainly in supine rest, explaining discrepant results in literature. In conclusion, we recommend estimating the whole alpha(n) spectrum before possibly providing the "two-exponent" description only.

Comparison of linear-stochastic and nonlinear-deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death

Objective:

Comparative algorithmic evaluation of heartbeat series in low-to-high risk cardiac patients for the prospective prediction of risk of arrhythmic death (AD).

Background:

Heartbeat variation reflects cardiac autonomic function and risk of AD. Indices based on linear stochastic models are independent risk factors for AD in post-myocardial infarction (post-MI) cohorts. Indices based on nonlinear deterministic models have superior predictability in retrospective data.

Methods:

Patients were enrolled (N = 397) in three emergency departments upon presenting with chest pain and were determined to be at low-to-high risk of acute MI (>7%). Brief ECGs were recorded (15 min) and R-R intervals assessed by three nonlinear algorithms (PD2i, DFA, and ApEn) and four conventional linear-stochastic measures (SDNN, MNN, 1/f-Slope, LF/HF). Out-of-hospital AD was determined by modified Hinkle–Thaler criteria.

Results:

All-cause mortality at one-year follow-up was 10.3%, with 7.7% adjudicated to be AD. The sensitivity and relative risk for predicting AD was highest at all time-points for the nonlinear PD2i algorithm (p ≤0.001). The sensitivity at 30 days was 100%, specificity 58%, and relative risk >100 (p ≤0.001); sensitivity at 360 days was 95%, specificity 58%, and relative risk >11.4 (p ≤0.001).

Conclusions:

Heartbeat analysis by the time-dependent nonlinear PD2i algorithm is comparatively the superior test.

Clinical impact of evaluation of cardiovascular control by novel methods of heart rate dynamics

Heart rate variability (HRV) has been conventionally analysed with time- and frequency-domain methods, which measure the overall magnitude of RR interval fluctuations around its mean value or the magnitude of fluctuations in some predetermined frequencies. Analysis of heart rate dynamics by novel methods, such as heart rate turbulence after ventricular premature beats, deceleration capacity of heart rate and methods based on chaos theory and nonlinear system theory, have gained recent interest. Recent observational studies have suggested that some indices describing nonlinear heart rate dynamics, such as fractal scaling exponents, heart rate turbulence and deceleration capacity, may provide useful prognostic information in various clinical settings and their reproducibility may be better than that of traditional indices. For example, the short-term fractal scaling exponent measured by the detrended fluctuation analysis method has been shown to predict fatal cardiovascular events in various populations. Similarly, heart rate turbulence and deceleration capacity have performed better than traditional HRV measures in predicting mortality in post-infarction patients. Approximate entropy, a nonlinear index of heart rate dynamics, which describes the complexity of RR interval behaviour, has provided information on the vulnerability to atrial fibrillation. There are many other nonlinear indices which also give information on the characteristics of heart rate dynamics, but their clinical usefulness is not as well established. Although the concepts of nonlinear dynamics, fractal mathematics and complexity measures of heart rate behaviour, heart rate turbulence, deceleration capacity in relation to cardiovascular physiology or various cardiovascular events are still far away from clinical medicine, they are a fruitful area for research to expand our knowledge concerning the behaviour of cardiovascular oscillations in normal healthy conditions as well as in disease states.

Levels of complexity in scale-invariant neural signals

Many physical and physiological signals exhibit complex scale-invariant features characterized by 1/f scaling and long-range power-law correlations, indicating a possibly common control mechanism. Specifically, it has been suggested that dynamical processes, influenced by inputs and feedback on multiple time scales, may be sufficient to give rise to 1/f scaling and scale invariance. Two examples of physiologic signals that are the output of hierarchical multiscale physiologic systems under neural control are the human heartbeat and human gait. Here we show that while both cardiac interbeat interval and gait interstride interval time series under healthy conditions have comparable 1/f scaling, they still may belong to different complexity classes. Our analysis of the multifractal scaling exponents of the fluctuations in these two signals demonstrates that in contrast to the multifractal behavior found in healthy heartbeat dynamics, gait time series exhibit less complex, close to monofractal behavior. Further, we find strong anticorrelations in the sign and close to random behavior for the magnitude of gait fluctuations at short and intermediate time scales, in contrast to weak anticorrelations in the sign and strong positive correlation for the magnitude of heartbeat interval fluctuations-suggesting that the neural mechanisms of cardiac and gait control exhibit different linear and nonlinear features. These findings are of interest because they underscore the limitations of traditional two-point correlation methods in fully characterizing physiological and physical dynamics. In addition, these results suggest that different mechanisms of control may be responsible for varying levels of complexity observed in physiological systems under neural regulation and in physical systems that possess similar 1/f scaling.

Heart rate variability explored in the frequency domain: a tool to investigate the link between heart and behavior

The neural regulation of circulatory function is mainly effected through the interplay of the sympathetic and vagal outflows. This interaction can be explored by assessing cardiovascular rhythmicity with appropriate spectral methodologies. Spectral analysis of cardiovascular signal variability, and in particular of RR period (heart rate variability, HRV), is a widely used procedure to investigate autonomic cardiovascular control and/or target function impairment. The oscillatory pattern which characterizes the spectral profile of heart rate and arterial pressure short-term variability consists of two major components, at low (LF, 0.04-0.15Hz) and high (HF, synchronous with respiratory rate) frequency, respectively, related to vasomotor and respiratory activity. With this procedure the state of sympathovagal balance modulating sinus node pacemaker activity can be quantified in a variety of physiological and pathophysiological conditions. Changes in sympathovagal balance can be often detected in basal conditions, however a reduced responsiveness to an excitatory stimulus is the most common feature that characterizes numerous pathophysiological states. Moreover the attenuation of an oscillatory pattern or its impaired responsiveness to a given stimulus can also reflect an altered target function and thus can furnish interesting prognostic markers. The dynamic assessment of these autonomic changes may provide crucial diagnostic, therapeutic and prognostic information, not only in relation to cardiovascular, but also non-cardiovascular disease. As linear methodologies fail to provide significant information in conditions of extremely reduced variability (e.g. strenuous exercise, heart failure) and in presence of rapid and transients changes or coactivation of the two branches of autonomic nervous system, the development of new non-linear approaches seems to provide a new perspective in investigating neural control of cardiovascular system.

Fractal scaling properties of heart rate dynamics following resistance exercise training

With aging and disease, there is a breakdown of the natural fractal-like organization of heart rate (HR). Fractal-like correlation properties of HR can be assessed with detrended fluctuation analysis (DFA). A short-time scaling exponent (αs) value of 1 is associated with healthy HR dynamics, whereas values that deviate away from 1, in either direction, indicate fractal collapse. The purpose of this study was to examine the effect of resistance exercise training (RT) on fractal correlation properties of HR dynamics. Resting ECG was collected at baseline, following a 4-wk time control period and 6 wk of RT (3 days per wk) in 34 men (23 ± 1 years of age). Fractal properties of HR were assessed with DFA. There was no change in αsfollowing either the time control period or RT (1.01 ± 0.06 to 0.98 ± 0.06 to 0.93 ± 0.04, P > 0.05). Given the potential bidirectional nature of fractal collapse, subjects were retrospectively separated into two groups (higher αsand lower αs) on the basis of the initial αsby using cluster analysis. An interaction was detected for αsfollowing RT ( P < 0.05). There was no change in αsin either group following the time control, but αsincreased following RT in the lower αsgroup ( n = 18; 0.73 ± 0.04 to 0.69 ± 0.04 to 0.88 ± 0.04) and αsdecreased following RT in the higher αsgroup ( n = 16; 1.20 ± 0.04 to 1.24 ± 0.04 to 0.98 ± 0.04). In conclusion, RT improves fractal properties of HR dynamics.

The circadian pacemaker generates similar circadian rhythms in the fractal structure of heart rate in humans and rats

AIMS

Adverse cardiovascular events in humans occur with a day/night pattern, presumably related to a daily pattern of behaviours or endogenous circadian rhythms in cardiovascular variables. Healthy humans possess a scale-invariant/fractal structure in heartbeat fluctuations that exhibits an endogenous circadian rhythm and changes towards the structure observed in cardiovascular disease at the circadian phase corresponding to the time of the broad peak of adverse cardiovascular events (at about 10 AM). To explore the relationship between the rest/activity cycle, endogenous circadian rhythmicity, and cardiac vulnerability, we tested whether the fractal structure of heart rate exhibits a similar circadian rhythm in a mammalian species that is nocturnally active (Wistar rats) compared with diurnally active humans, and how this fractal structure changes after lesioning the circadian pacemaker (suprachiasmatic nucleus, SCN) in rats.

METHODS AND RESULTS

Analysis of heart rate data collected over 10 days in eight intact and six SCN-lesioned Wistar rats during constant darkness revealed that: (i) as with humans, rats exhibit an endogenous circadian rhythm in the scaling exponent characterizing the hourly fractal structure of heart rate (P = 0.0005) with larger exponents during the biological day (inactive phase for rats; active phase for humans); (ii) SCN lesioning abolished the rhythm in the fractal structure of heart rate and systematically increased the scaling exponent (P = 0.01).

CONCLUSION

Rats possess a circadian rhythm of fractal structure of heart rate with a similar temporal pattern as previously observed in humans despite opposite rest/activity cycles between the two species. The SCN imparts this endogenous rhythm. Moreover, lesioning the SCN in rats results in a larger scaling exponent, as occurs with cardiovascular disease in humans.

Conditions of autonomic reciprocal interplay versus autonomic co-activation: Effects on non-linear heart rate dynamics

The present study was aimed at investigating the autonomic nervous system influences on the fractal organization of human heart rate during sympathovagal interactions, with special emphasize on the short-term fractal organization in heart rate variability (HRV), as assessed by the scaling exponent (alpha(1)) of the detrended fluctuation analysis. Linear and non-linear HRV analyses were used to study the sympathetic and vagal modulation of heart rate in ten healthy men (mean +/- SEM; age 26 +/- 1 years) during conditions of 1) increased sympathetic activity and vagal withdrawal (head-up tilt), 2) decreased sympathetic activity and increased vagal outflow (thermoneutral upright head-out water immersion, WIn), and 3) simultaneous activation of the two arms of the autonomic nervous activity (upright head-out immersion in cold water, WIc). Hemodynamic and linear HRV results were consistent with previous reports during similar physiological conditions. alpha(1) increased significantly during head-up tilt (from 0.71 +/- 0.13 supine to 0.90 +/- 0.15 upright) and WIn (0.86 +/- 0.10) and was significantly decreased during WIc (0.61 +/- 0.15). Thus, alpha(1) increased when the cardiac autonomic interplay was altered in a reciprocal fashion, whatever the direction of the balance change. Conversely, alpha(1) decreased during the concomitant activation of both vagal and sympathetic activities. The results of linear analysis were necessary to precisely define the direction of change in autonomic control revealed by an increase in alpha(1), while the direction of change in alpha(1) indicated whether an increased vagal activity is coupled with a decreased or increased sympathetic activation. Using both linear and non-linear analysis of HRV may increase the understanding of changes in cardiac autonomic status.

Local-scale analysis of cardiovascular signals by detrended fluctuations analysis: effects of posture and exercise

The fractal structure of heart rate is usually quantified by estimating a short-term (alpha(1)) and a long-term (alpha(2)) scaling exponent by Detrended Fluctuations Analysis (DFA). Evidence, however, has been provided that heart rate is a multifractal signal, better characterized by a large number of scaling exponents. Aim of this study is to verify whether two scaling exponents only from DFA provide a sufficiently accurate description of the possibly multifractal nature of cardiovascular signals. We measured ECG and finger arterial pressure in 33 volunteers for 10 minutes during each of 3 conditions: supine rest (SUP); sitting at rest (SIT); light physical exercise (EXE). DFA was applied on the beat-by-beat series of R-R interval (RRI) and mean arterial pressure (MAP). We then computed the local scaling exponent alpha(n), defined as the slope of the detrended fluctuation function F(n) around the beat scale n, in a log-log plot. If alpha(1) and alpha(2) correctly model the multiscale structure of blood pressure and heart rate, we should find that alpha(n) is constant over a short-term and a longterm range of beat scales. Results show that only the long-term alpha2 exponent provides a relatively good approximation of the multiscale structure of RRI and MAP. Moreover, posture and physical activity have important effects on local scaling exponents, and on the range of beat scales n where alpha(n) can be approximated by a constant alpha2 coefficient.

Predicting Imminent Episodes of Ventricular Tachyarrhythmia Using Heart Rate

BACKGROUND

A reliable predictor of an imminent episode of ventricular tachyarrhythmia that could be incorporated in an implantable defibrillator capable of preventive therapy would have important clinical utility.

METHOD

A test set of 208 R-R records saved by defibrillators spanning a mean of 1.6 hours before sustained tachyarrhythmia were used to derive criteria that would improve the specificity of the previously identified monotonic heart rate acceleration predictor. Additional criteria were used, namely two such patterns need to occur within a period of 1.8 hour and the heart rate during these accelerations exceeds 86 bpm (700 ms). The specificity was tested using R-R records matched in duration from 26 control patients with defibrillators during normal periods.

RESULTS

The basic acceleration pattern was found during sinus rhythm in the 1.8-hour period prior to 83% of episodes of ventricular tachyarrhythmia. It was also found in 43% of the matched set of non-arrhythmic records, corresponding to a specificity of 57%. With the two extra requirement of multiplicity within 1.8 hour and peak heart rate, the sensitivity of the proposed predictor is reduced to 53%, but the specificity is increased to 91%, which corresponds to an average false positive rate of 0.8 event/day across the patient population.

CONCLUSION

A ventricular tachyarrhythmia predictor based on a pattern of heart rate acceleration has been proposed that can yield sensitivity from 53% to 69%, with specificity up to 91%. Instances of this predictor increase significantly prior to an episode of tachyarrhythmia.

Comparison of methods for editing of ectopic beats in measurements of short-term non-linear heart rate dynamics

Non-linear heart rate (HR) dynamics characterizes the fractal properties and complexity of the variations in HR. Ventricular and supraventricular ectopic beats might introduce a mathematical artefact to the analyses on sinus rhythm. We therefore evaluated the effects of different editing practices for ectopic beats such that 753 40-min ECG recordings were (i) not edited for the ectopic beats, or the ectopic beats were edited with (ii) an interpolation or with (iii) a deletion method before the analyses of non-linear HR dynamics. The non-linear HR dynamics analyses included detrended fluctuation analysis (DFA), approximate entropy, symbolic dynamics (SymDyn), fractal dimension and return map (RM). We found that the short-term scaling exponent (alpha1) of DFA, forbidden words of SymDyn and RM were sensitive measurements to the ectopic beats and there were strong correlations between these measurements and the number of ectopic beats. In addition, the unedited ectopic beats significantly lowered the stability of these measurements. However, the editing either with interpolation or deletion method corrected the measurements for the bias caused by the ectopic beats. On the contrary, the entropy measurements were not as sensitive to the ectopic beats. In conclusion, the ectopic beats affect the non-linear HR dynamics of sinus rhythm differently, causing a more marked bias in fractal than in complexity measurements of non-linear HR dynamics. This erroneous effect of ectopic beats can be corrected with a proper editing of these measurements. Therefore, there is an obvious need for standardized editing practices for ectopic beats before the analysis of non-linear HR dynamics.

Complexity analysis of 24 hours heart rate variability time series

We propose to study the heart rate variability (HRV) time series complexity by computing the Lempel Ziv complexity measure. LZ is sensitive to the rate of pattern recurrences in a time series. Analysis considers 24 h HRV time series of healthy subjects and patients with cardiovascular diseases. Analysis with simulated signals show the LZ measure can vary depending on the adopted coding process. The binary coding, proposed in this work, is sensitive to the different dynamical systems generating the time series, as the ternary coding is sensitive to the presence of stationary states, i.e. a consecutive repetition of the same RR interval value. LZ method reliably differentiates healthy vs. disease group. Further clinical investigations on the LZ complexity and on its relationship to the risk of sudden death, can supply new diagnostic indications.

The role of heart rate variability in prognosis for different modes of death in chronic heart failure

Classic risk factors for mortality due to chronic heart failure (CHF), such as low left ventricular ejection fraction, NYHA functional stage, and increased heart rate perform well in the prediction of death from pump failure. The prediction of sudden cardiac death (SCD) remains somewhat problematic. Numerous studies have analyzed the potential contribution heart rate variability (HRV) can make to risk assessment in CHF. The aim of this review was to summarize the literature and identify the role HRV might play in identifying mode of death, as well as overall mortality risk. In studies where all-cause mortality or cardiac events were the clinical end point(s), global and slow oscillatory measures of HRV were the strongest risk predictors. In the fewer studies that used SCD as an end point, the strongest risk factors were HRV measures of short-term oscillations and sympathovagal interaction. We concluded from these findings that different HRV measurements predict different modes of death in CHF.Additionally, further studies using short-term analysis of HRV and non-linear analyses are warranted. Furthermore, studies with multiple end points, which clearly delineate pump failure from SCD, may be useful to identify more clearly the role HRV measures can play in the prediction of SCD.