Spectral analysis of R-R interval variability in inspiratory breath holding in man at rest and during emotional strain

30 young males performed inspiratory breath holdings during expectation of an aversive stimulus and at relative rest. The consecutive R-R intervals of the ECG from breath-hold trial were analysed via spectral analysis of time series. Following parameters were ascertained for each breath holding: mean R-R interval, total R-R interval variability, breath-hold time and relative variability in three spectral bands 3-8 s, 8-12 s and 12-18 s. Neither of these variables was influenced by expectation of an aversive stimulus. The data were subsequently analysed by means of multivariate analysis. Three distinct frequency components were selected according to both histogram data and multivariate analysis. Their modal periods were 5-6 s, 12 s and 16 s respectively. The 8-12 s component of R-R interval variability dominated during breath holdings. The 3-8 s band bore a negative relationship to breath-hold time.     

Sympathetic nervous system representation in time and frequency domain indices of heart rate variability

This study evaluated the contributions of sympathetic and parasympathetic modulation to heart rate variability during situations in which vagal and sympathetic tone predominated. In a placebo-controlled, randomized, double blind blockade study, six young healthy male individuals received propranolol (0.2 mg x kg(-1)), atropine (0.04 mg x kg(-1)), propranolol plus atropine, or placebo infusions over 4 days. Time-domain indices were calculated during 40 min of rest and 20 min of exercise at 70% of maximal exercise intensity. Spectrum analysis, using fast Fourier transformation, was also performed at rest and during the exercise. The time-domain indices standard deviation of R-R intervals, mean of the standard deviations of all R-R intervals for all 5-min segments, percentage of number of pairs of adjacent R-R intervals differing by more than 50 ms, and square root of the mean of the sum of squares of differences between adjacent R-R intervals were reduced after atropine and propranolol plus atropine. Propranolol alone caused no appreciable change in any of the time-domain indices. At rest, all spectrum components were similar after placebo and propranolol infusions, but following parasympathetic and double autonomic blockade there was a reduction in all components of the spectrum analysis, except for the low:high ratio. During exercise, partial and double blockade did not change significantly any of the spectrum components. Thus, time and frequency-domain indices of heart rate variability were able to detect vagal activity, but could not detect sympathetic activity. During exercise, spectrum analysis is not capable of evaluating autonomic modulation of heart rate.     

Heart rate variability analysis in general medicine

Autonomic nervous system plays an integral role in homeostasis. Autonomic modulation can frequently be altered in patients with cardiac disorders as well as in patients with other critical illnesses or injuries. Assessment of heart rate variability is based on analysis of consecutive normal R-R intervals and may provide quantitative information on the modulation of cardiac vagal and sympathetic nerve input. The hypothesis that depressed heart rate variability may occur over a broad range of illness and injury, and may inversely correlated with disease severity and outcome has been tested in various clinical settings over the last decade. This article reviews recent literature concerning the potential clinical implications and limitations of heart rate variability assessment in general medicine.     

Saturation of high-frequency oscillations of R-R intervals in healthy subjects and patients after acute myocardial infarction during ambulatory conditions

This study was designed to assess the relationship between R-R interval length and heart rate (HR) variability in healthy subjects and patients after an acute myocardial infarction (AMI). Twenty-four-hour ambulatory ECG recordings were obtained for 76 healthy subjects and 82 post-AMI patients. The high-frequency (HF, 0.15-0.4 Hz) spectral power of R-R intervals was analyzed in 5-min sequences over 24 h and plotted as a function of the corresponding mean R-R interval length. Quadratic regression model was used to study the relationship between R-R interval length and HF power. If a distinct deflection point (R-R0) occurred in the quadratic regression (r >0.50) model before maximum R-R interval, indicating the plateau of HF power, the relationship between R-R interval and HF power was defined as saturated. Otherwise, the relationship was defined as linear (r >0.50) or low correlated (r >0.50). The relationship was saturated in 35, linear in 38, and low correlated in 3 healthy subjects. In post-AMI patients, the relationship was saturated in 9 subjects, linear in 44 subjects, and low correlated in 29 patients. The HF power analyzed from the 24-h period did not differ between the saturated and linear groups, but when analyzed from the linear portion only, HF spectral power was smaller in the linear than the saturated group both among healthy subjects (P <0.05) and post-AMI patients (P <0.05). Saturation of the HF oscillations of R-R intervals is a common phenomenon in healthy subjects and also present in post-AMI patients during ambulatory conditions. This saturation effect may bias the quantification of cardiac vagal function when HR variability is analyzed from Holter recordings.     

Poincaré plot interpretation using a physiological model of HRV based on a network of oscillators

In this paper, we develop a physiological oscillator model of which the output mimics the shape of the R-R interval Poincaré plot. To validate the model, simulations of various nervous conditions are compared with heart rate variability (HRV) data obtained from subjects under each prescribed condition. For a variety of sympathovagal balances, our model generates Poincaré plots that undergo alterations strongly resembling those of actual R-R intervals. By exploiting the oscillator basis of our model, we detail the way that low- and high-frequency modulation of the sinus node translates into R-R interval Poincaré plot shape by way of simulations and analytic results. With the use of our model, we establish that the length and width of a Poincaré plot are a weighted combination of low- and high-frequency power. This provides a theoretical link between frequency-domain spectral analysis techniques and time-domain Poincaré plot analysis. We ascertain the degree to which these principles apply to real R-R intervals by testing the mathematical relationships on a set of data and establish that the principles are clearly evident in actual HRV records.     

Nonstationarity and duration of the R-R-segment in the diagnostics of functional states of operators

The effect of the nonstationarity of R-R interval series on the diagnostics of functional states of operators has been analyzed. The functional states were diagnosed by means of a factor model of heart rate variability. The heart rate was recorded in the supine position, before the performance of an important task, and after its completion. A high resistance of the diagnostics of functional states to nonstationarity was found for all periods. Indices of heart rate variability resistant to nonstationarity were defined. Also, the effect of R-R segment duration on functional states diagnostics was explored. The results obtained allow one to conclude that the diagnostics of functional states based on the three-factor model of heart rate variability can be used on short segments within a range of 256 divided 32 R-R intervals. The indices of the factor model of heart rate variability must be normalized for corresponding R-R segment duration before diagnostics. In addition, the effect of the duration of R-R segment on the indices of heart rate variability was analyzed for different functional states. The indices resistant to the duration of R-R segments and conditions necessary for heart rate recording were defined.     

Assessment of parasympathetic reactivation after exercise

The objective of this study was to evaluate whether heart rate variability (HRV) can be used as an index of parasympathetic reactivation after exercise. Heart rate recovery after exercise has recently been shown to have prognostic significance and has been postulated to be related to abnormal recovery of parasympathetic tone. Ten normal subjects [5 men and 5 women; age 33 +/- 5 yr (mean +/- SE)] exercised to their maximum capacity, and 12 subjects (10 men and 2 women; age 61 +/- 10 yr) with coronary artery disease exercised for 16 min on two separate occasions, once in the absence of atropine and once with atropine (0.04 mg/kg) administered during exercise. The root mean square residual (RMS), which measures the deviation of the R-R intervals from a straight line, as well as the standard deviation (SD) and the root mean square successive difference of the R-R intervals (MSSD), were measured on successive 15-, 30-, and 60-s segments of a 5-min ECG obtained immediately after exercise. In recovery, the R-R interval was shorter with atropine (P < 0.0001). Without atropine, HRV, as measured by the MSSD and RMS, increased early in recovery from 4.1 +/- 0.4 and 3.7 +/- 0.4 ms in the first 15 s to 7.2 +/- 1.0 and 7.4 +/- 0.9 ms after 1 min, respectively (P < 0.0001). RMS (range 1.7-2.1 ms) and MSSD were less with atropine (P < 0.0001). RMS remained flat throughout recovery, whereas MSSD showed some decline over time from 3.0 to 2.2 ms (P < 0.002). RMS and MSSD were both directly related (r(2) = 0.47 and 0.56, respectively; P < 0.0001) to parasympathetic effect, defined as the difference in R-R interval without and with atropine. In conclusion, RMS and MSSD are parameters of HRV that can be used in the postexercise recovery period as indexes of parasympathetic reactivation after exercise. These tools may improve our understanding of parasympathetic reactivation after exercise and the prognostic significance of heart rate recovery.     

A pilot investigation of the effect of extremely low frequency pulsed electromagnetic fields on humans' heart rate variability

The question whether pulsed electromagnetic field (PEMF) can affect the heart rhythm is still controversial. This study investigates the effects on the cardiocirculatory system of ELF-PEMFs. It is a follow-up to an investigation made of the possible therapeutic effect ELF-PEMFs, using a commercially available magneto therapeutic unit, had on soft tissue injury repair in humans. Modulation of heart rate (HR) or heart rate variability (HRV) can be detected from changes in periodicity of the R-R interval and/or from changes in the numbers of heart-beat/min (bpm), however, R-R interval analysis gives only a quantitative insight into HRV. A qualitative understanding of HRV can be obtained considering the power spectral density (PSD) of the R-R intervals Fourier transform. In this study PSD is the investigative tool used, more specifically the low frequency (LF) PSD and high frequency (HF) PSD ratio (LF/HF) which is an indicator of sympatho-vagal balance. To obtain the PSD value, variations of the R-R time intervals were evaluated from a continuously recorded ECG. The results show a HR variation in all the subjects when they are exposed to the same ELF-PEMF. This variation can be detected by observing the change in the sympatho-vagal equilibrium, which is an indicator of modulation of heart activity. Variation of the LF/HF PSD ratio mainly occurs at transition times from exposure to nonexposure, or vice versa. Also of interest are the results obtained during the exposure of one subject to a range of different ELF-PEMFs. This pilot study suggests that a full investigation into the effect of ELF-PEMFs on the cardiovascular system is justified.     

AR identification and spectral estimate applied to the R-R interval measurements

The methods of identification and spectral estimate are applied to the tachogram, i.e. the time series constituted by the cycle-by-cycle R-R interval durations measured on the ECG signal from cardiological patients in ambulatory rehabilitation training after episodes of myocardial infarction or ischemic disease. The Batch Least Squares Method is applied to identify the series as an AR process of 5th order. The whiteness test and Rissanen's optimization criterion are also fulfilled. The clinical information is in this way highly compressed in the pole diagram and in the Maximum Entropy Spectrum (MES) estimated on the basis of the AR coefficients. The experimental results in a restricted set of patients confirm the feasibility of new instrumentation design criteria for non-conventional R-R intervals parametrisation, successive diagnostic classification and beat prediction. Finally, some preliminary considerations about the capabilities of the introduced methods put into evidence the role of computerized techniques in recognizing the fundamental patterns of physiopathological heart rate variability, which the usual conventional methods of ECG analysis are not able to detect in a reliable way.     

Fractal and periodic heart rate dynamics in fetal sheep: comparison of conventional and new measures based on fractal analysis

The physiological significance of spectral and fractal components of spontaneous heart rate (HR) variability in the fetus remains unclear. To examine the relationship between circadian rhythms in different measures of HR variability, R-R interval time series obtained by fetal ECGs were recorded continuously over 24 h in five pregnant sheep at 116-125 days gestation. Conventional measures of short-term (STV) and long-term variability (LTV), low-frequency (LF; 0.025-0.15 cycles/beat) and high-frequency (HF; 0.2-0.5 cycles/beat) spectral powers, the LF-to-HF ratio, and fractal dimension values were calculated from 24-h ECG recordings and quantified every 60 min. STV, LTV, and LF and HF spectral powers were minimal during the day but increased significantly to their highest values at night. We found a significant positive correlation between these measures, whereas the cosinor method showed significant similarity between their circadian rhythm patterns. Fetal R-R intervals also exhibited fractal structures. Fetal HR variability had a fractal structure, which was similar between day and night. These results suggested that the circadian rhythms exhibited by STV and LTV during the day were mainly due to changes in frequency components rather than to fractal components of fetal HR fluctuation.     

Accuracy of the StressEraser<Superscript>®</Superscript> in the Detection of Cardiac Rhythms

StressEraser is a commercially marketed biofeedback device designed to enhance heart rate variability. StressEraser makes its internal calculations on beat-to-beat measures of finger pulse intervals. However, the accuracy and precision of StressEraser in quantifying interbeat intervals using finger pulse intervals has not been evaluated against standard laboratory equipment using R-R intervals. Accuracy was assessed by simultaneously recording interbeat intervals using StressEraser and a standard laboratory ECG system. The interbeat intervals were highly correlated between the systems. The average deviation in interbeat interval recordings between the systems was approximately 6 ms. Moreover, correlations approached unity between the systems on estimates of heart period, heart rate, and heart rate variability. Feedback from StressEraser is based on an interbeat time series that provides sufficient information to provide an excellent estimate of the dynamic changes in heart rate and heart rate variability. The slight variations between StressEraser and the laboratory equipment in quantifying heart rate and heart rate variability are due to features related to monitoring heart rate with finger pulse: (1) a lack in precision in the peak of the finger pulse relative to the clearly defined inflection point in the R-wave, and (2) contribution of variations in pulse transit time.     

Correlation properties of heartbeat dynamics

In this study, we investigate correlation properties of fluctuations in heart interbeat (RR) time series in a broad range of physiological and pathological conditions. Using detrended fluctuation analysis (DFA) method we determined short-term (alpha (1)) and long-term (alpha (2)) scaling exponent. In addition, we calculated standard deviation of RR intervals (SDRR) as the simplest variability measure. We found that the difference between alpha (1) and alpha (2) is related to RR interval length. At the shortest RR intervals, which correspond to extreme physiological and pathological conditions, we found the highest reduction of variability and the biggest difference between scaling exponents. In this case, DFA reveals a white noise over short scales (alpha (1 )about 0.5) and strongly correlated noise over large scales (alpha (2) about 1.5). With an increase in RR interval, accompanied by increased variability (increase in parasympathetic control), the difference between alpha (1) and alpha (2) decreases. The difference between scaling exponents disappeared in a state of efficient autonomic control. We suggest that the complexity in heart rhythm is achieved through coupling between intrinsically controlled heart rhythm and autonomic control, and that the model of stochastic resonance mechanism could be applied to this system.     

Nonlinear modeling of the atrioventricular node physiology in atrial fibrillation

A nonlinear model of the atrioventricular (AV) node physiology in atrial fibrillation (AF) is proposed based on three assumptions: (1) normal distribution of atrial impulses, (2) right-skewed distribution of R-R intervals, (3) increase in the refractory period of the AV node due to rapid bombardment from the atria. Simulation resulted in the following conclusions, all of which are in agreement with previous experience: (1) the entry speed of atrial impulses into the AV node in AF is inversely proportional to the ventricular rate, (2) the autocorrelation function of R-R intervals is zero at all delays, (3) a newly introduced index, sign of first difference, has a negative autocorrelation function at the first delay and zero ones at all others. In spite of its simplicity, the model is able to explain what happens in atrial premature complexes, sinus tachycardia and sinus bradycardia. Different rhythms, some of which rarely seen clinically, can be reproduced by changing input patterns or by slightly manipulating the model parameters. In order to make possible a long irregular time series of R-R interval, aperiodic changes in atrial signals are shown to be necessary. In conclusion, we proposed a simple model for the AV node physiology capable of explaining the previously known facts about AF as well as predicting interesting properties of some other supraventricular arrhythmias.     

Running average analysis of clinical trial ambulatory blood pressure data

A method is presented for analyzing ambulatory blood pressure monitoring (ABPM) time series data obtained from well-controlled clinical trials. The method uses running averages based on fixed time-of-day intervals (rather than a fixed number of neighboring measurements). These "interval running averages" effectively estimate average blood pressure during the specified time intervals, adjusting for unequal spacing between measurements, embedded missing data, varying measurement times-of-day, and doses of study medication taken during ABP monitoring. Blood pressure changes from baseline may be computed using the interval running averages in order to separate treatment effects from patients' normal daily blood pressure cycles. To ensure valid estimation of treatment effects over time, study medication dosing times should be rigorously controlled in the trial design and conduct. Interval running average curves may be presented graphically, and from them summary statistics may be computed for purposes of statistical analysis. By allowing for the inherent complications of ABP data collection, the effect of antihypertensive treatment in well-controlled clinical trials can be discerned.     

Respiratory modulation of human autonomic rhythms

We studied the influence of three types of breathing [spontaneous, frequency controlled (0.25 Hz), and hyperventilation with 100% oxygen] and apnea on R-R interval, photoplethysmographic arterial pressure, and muscle sympathetic rhythms in nine healthy young adults. We integrated fast Fourier transform power spectra over low (0.05-0.15 Hz) and respiratory (0.15-0.3 Hz) frequencies; estimated vagal baroreceptor-cardiac reflex gain at low frequencies with cross-spectral techniques; and used partial coherence analysis to remove the influence of breathing from the R-R interval, systolic pressure, and muscle sympathetic nerve spectra. Coherence among signals varied as functions of both frequency and time. Partialization abolished the coherence among these signals at respiratory but not at low frequencies. The mode of breathing did not influence low-frequency oscillations, and they persisted during apnea. Our study documents the independence of low-frequency rhythms from respiratory activity and suggests that the close correlations that may exist among arterial pressures, R-R intervals, and muscle sympathetic nerve activity at respiratory frequencies result from the influence of respiration on these measures rather than from arterial baroreflex physiology. Most importantly, our results indicate that correlations among autonomic and hemodynamic rhythms vary over time and frequency, and, thus, are facultative rather than fixed.     

Fractal properties of human muscle sympathetic nerve activity

Muscle sympathetic nerve activity (MSNA) in resting humans is characterized by cardiac-related bursts of variable amplitude that occur sporadically or in clusters. The present study was designed to characterize the fluctuations in the number of MSNA bursts, interburst interval, and burst amplitude recorded from the peroneal nerve of 15 awake, healthy human subjects. For this purpose, we used the Allan and Fano factor analysis and dispersional analysis to test whether the fluctuations were time-scale invariant (i.e., fractal) or random in occurrence. Specifically, we measured the slopes of the power laws in the Allan factor, Fano factor, and dispersional analysis curves. In addition, the Hurst exponent was calculated from the slope of the power law in the Allan factor curve. Whether the original time series contained fractal fluctuations was decided on the basis of a comparison of the values of these parameters with those for surrogate data blocks. The results can be summarized as follows. Fluctuations in the number of MSNA bursts and interburst interval were fractal in each of the subjects, and fluctuations in burst amplitude were fractal in four of the subjects. We also found that fluctuations in the number of heartbeats and heart period (R-R interval) were fractal in each of the subjects. These results demonstrate for the first time that apparently random fluctuations in human MSNA are, in fact, dictated by a time-scale-invariant process that imparts "long-term memory" to the sequence of cardiac-related bursts. Whether sympathetic outflow to the heart also is fractal and contributes to the fractal component of heart rate variability remains an open question.     

Entropies of short binary sequences in heart period dynamics

Dynamic aspects of R-R intervals have often been analyzed by means of linear and nonlinear measures. The goal of this study was to analyze binary sequences, in which only the dynamic information is retained, by means of two different aspects of regularity. R-R interval sequences derived from 24-h electrocardiogram (ECG) recordings of 118 healthy subjects were converted to symbolic binary sequences that coded the beat-to-beat increase or decrease in the R-R interval. Shannon entropy was used to quantify the occurrence of short binary patterns (length N = 5) in binary sequences derived from 10-min intervals. The regularity of the short binary patterns was analyzed on the basis of approximate entropy (ApEn). ApEn had a linear dependence on mean R-R interval length, with increasing irregularity occurring at longer R-R interval length. Shannon entropy of the same sequences showed that the increase in irregularity is accompanied by a decrease in occurrence of some patterns. Taken together, these data indicate that irregular binary patterns are more probable when the mean R-R interval increases. The use of surrogate data confirmed a nonlinear component in the binary sequence. Analysis of two consecutive 24-h ECG recordings for each subject demonstrated good intraindividual reproducibility of the results. In conclusion, quantification of binary sequences derived from ECG recordings reveals properties that cannot be found using the full information of R-R interval sequences.     

The surgeon's mental load during decision making at various stages of operations

In surgeons while opening the wound, during the operation proper, closing the skin and immediately after the operation, the ECG was recorded telemetrically for 5-min periods. From the ECG recordings, indices reflecting cardiac arrhythmia and emotional level were calculated. It is concluded that the process of decision making during the vital stages of operations causes a fall in the CHRV (the coefficient of heart rate variability), S2 (the variance of R-R intervals) and VR (the variability range of R-R intervals). It seems that of the indices studied, the most suitable for evaluation of the degree of mental loading due to decision making processes are the CHRV and S2. During all the stages of surgery studied, and immediately after the operation, an increase in tonus of the sympathetic nervous system occurs in surgeons indicating a rise in emotional level.     

Gene conversion and crossing over along the 405-kb left arm of Saccharomyces cerevisiae chromosome VII

Gene conversions and crossing over were analyzed along 10 intervals in a 405-kb region comprising nearly all of the left arm of chromosome VII in Saccharomyces cerevisiae. Crossover interference was detected in all intervals as measured by a reduced number of nonparental ditypes. We have evaluated interference between crossovers in adjacent intervals by methods that retain the information contained in tetrads as opposed to single segregants. Interference was seen between intervals when the distance in the region adjacent to a crossover was < approximately 35 cM (90 kb). At the met13 locus, which exhibits approximately 9% gene conversions, those gene conversions accompanied by crossing over exerted interference in exchanges in an adjacent interval, whereas met13 gene conversions without an accompanying exchange did not show interference. The pattern of exchanges along this chromosome arm can be represented by a counting model in which there are three nonexchange events between adjacent exchanges; however, maximum-likelihood analysis suggests that approximately 8-12% of the crossovers on chromosome VII arise by a separate, noninterfering mechanism.