Nonlinear dynamics of heart rate variability during experimental hemorrhage in ketamine-anesthetized rats

Indexes of heart rate variability (HRV) based on linear stochastic models are independent risk factors for arrhythmic death (AD). An index based on a nonlinear deterministic model, a reduction in the point correlation dimension (PD2i), has been shown in both animal and human studies to have a higher sensitivity and specificity for predicting AD. Dimensional reduction subsequent to transient ischemia was examined previously in a simple model system, the intrinsic nervous system of the isolated rabbit heart. The present study presents a new model system in which the higher cerebral centers are blocked chemically (ketamine inhibition of N-methyl-D-aspartate receptors) and the system is perturbed over a longer 15-min interval by continuous hemorrhage. The hypothesis tested was that dimensional reduction would again be evoked, but in association with a more complex relationship between the system variables. The hypothesis was supported, and we interpret the greater response complexity to result from the larger autonomic superstructure attached to the heart. The complexities observed in the nonlinear heartbeat dynamics constitute a new genre of autonomic response, one clearly distinct from a hardwired reflex or a cerebrally determined defensive reaction.     

Determinants of heart rate variability in obstructive sleep apnea syndrome during wakefulness and sleep

Heart rate variability (HRV) is mediated by at least three primary mechanisms: 1) vagal feedback from pulmonary stretch receptors (PSR), 2) central medullary coupling between respiratory and cardiovagal neurons (RCC), and 3) arterial baroreflex (ABR)-induced fluctuations. We employed a noninvasive experimental protocol in conjunction with a minimal model to determine how these sources of HRV are altered in obstructive sleep apnea syndrome (OSAS). Respiration, heart rate, and blood pressure were monitored in eight normal subjects and nine untreated OSAS patients in relaxed wakefulness and stage 2 and rapid eye movement sleep. A computer-controlled ventilator delivered inspiratory pressures that varied randomly from breath to breath. Application of the model to the corresponding subject responses allowed the delineation of the three components of HRV. In all states, RCC gain was lower in OSAS patients than in normal subjects (P < 0.04). ABR gain was also reduced in OSAS patients (P < 0.03). RCC and ABR gains increased from wakefulness to sleep (P < 0.04). However, there was no difference in PSR gain between subject groups or across states. The findings of this study suggest that the adverse autonomic effects of OSAS include impairment of baroreflex gain and central respiratory-cardiovascular coupling, but the component of respiratory sinus arrhythmia that is mediated by lung vagal feedback remains intact.     

Decreased fractal component of human heart rate variability during non-REM sleep

The physiological significance of the fractal component of short-term, spontaneous heart rate variability (HRV) in humans remains unclear. The aim of the present study was to gain further information about the respective fractal components by extracting them from HRV, blood pressure variability (BPV), and instantaneous lung volume (ILV) time series via coarse graining spectral analysis in nine healthy subjects during waking and sleep states. The results show that the contribution made by the fractal component to the total variance in the beat-to-beat R-R interval declined significantly as the depth of non-rapid eye movement (non-REM) sleep increased, that the ILV time series was largely periodic (i.e., nonfractal), and that BPV was unaffected by sleep stage. Finally, the fractal component of HRV during REM sleep was found to be quite similar to that seen during waking. These results suggest that mechanisms involving electroencephalographic desynchronization and/or conscious states of the brain are reflected in the fractal component of HRV.     

Spectral analysis of the heart rate variability in sleep

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

Inhibition of heart rate variability during sleep in humans by 6700 K pre-sleep light exposure

Two different spectral analyses of heart rate (HR) variability (HRV) were performed on seven young male subjects to evaluate the effects of different color temperatures of light exposure (6700 K, 5000 K, 3000 K) before sleep on cardiac vagal activity. In investigating HRV, we used an ordinary fast Fourier transform (FFT) and coarse graining spectral analysis (CGSA), which selectively extracts random fractal components from a given time series. The results showed that suppressions of HR during sleep after 6700 K light exposure were more inhibited than the other two lighting conditions. Increases in high-frequency (HF) components of HRV during sleep were also inhibited by 6700 K pre-sleep lighting. These results indicate that pre-sleep exposure to light of a higher color temperature may inhibit the enhancement of cardiac vagal activity during sleep. Moreover, significant HF alterations were shown in fractal-free HF (not in ordinary HF) components by CGSA. Because the HF component originates from respiratory sinus arrhythmia with periodical fluctuations, CGSA may be an appropriate approach for HRV evaluation during sleep.     

Heart rate variability in healthy humans during night sleep

Polysomnography has been performed and heart rate variability has been studied during night sleep in healthy young subjects of both sexes. It has been shown that, on average, a shift in autonomic support towards parasympathicotonia occurs during sleep, with a maximum at stages III and IV of NREM sleep. At stage II of NREM sleep and during REM sleep, a short-term activation of the sympathetic nervous system comparable to wakefulness is observed.     

Respiratory influences on non-linear dynamics of heart rate variability in humans

 The goal of our study was to determine whether evidence for chaos in heart rate variability (HRV) can be observed when the respiratory input to the autonomic controller of heart rate is forced by the deterministic pattern associated with periodic breathing. We simultaneously recorded, in supine healthy volunteers, RR intervals and breathing volumes for 20 to 30 min (1024 data point series) during three protocols: rest (control), fixed breathing (15 breath/min) and voluntary periodic breathing (3 breaths with 2 s inspiration and 2 s expiration followed by an 8 s breath hold). On both the RR interval and breathing volume series we applied the non-linear prediction method (Sugihara and May algorithm) to the original time series and to distribution-conserved isospectral surrogate data. Our results showed that, in contrast to the control test, during both fixed and voluntary periodic breathing the variability of breathing volumes was clearly deterministic non-chaotic. During all the three protocols, the RR-interval series’ non-linear predictability was consistent with one of a chaotic series. However, at rest, no clear difference was observed between the RR-interval series and their surrogates, which means that no clear chaos was observed. During fixed breathing a difference appeared, and this difference seemed clearer during voluntary periodic breathing. We concluded that HRV dynamics were chaotic when respiration was forced with a deterministic non-chaotic pattern and that normal spontaneous respiratory influences might mask the normally chaotic pattern in HRV. Received: 7 August 1995 / Accepted in revised form: 20 March 1997     

Autonomic control of heart rate during exercise studied by heart rate variability spectral analysis.

Spectral analysis of heart rate variability (HRV) might provide an index of relative sympathetic (SNS) and parasympathetic nervous system (PNS) activity during exercise. Eight subjects completed six 17-min submaximal exercise tests and one resting measurement in the upright sitting position. During submaximal tests, work rate (WR) was increased for the initial 3 min in a ramp fashion until it reached constant WRs of 20 W, or 30, 60, 90, 100, and 110% of the predetermined ventilatory threshold (Tvent). Ventilatory profile and alveolar gas exchange were monitored breath by breath, and beat-to-beat HRV was measured as R-R intervals of an electrocardiogram. Spectral analysis was applied to the HRV from 7 to 17 min. Low-frequency (0-0.15 Hz) and high-frequency (0.15-1.0 Hz) areas under power spectra (LO and HI, respectively) were calculated. The indicator of PNS activity (HI) decreased dramatically (P less than 0.05) when the subjects exercised compared with rest and continued to decrease until the intensity reached 60% Tvent. The indicator of SNS activity (LO/HI) remained unchanged up to 100% Tvent, whereas it increased abruptly (P less than 0.05) at 110% Tvent. The results suggested that (cardiac) PNS activity decreased progressively from rest to a WR equivalent to 60% Tvent, and SNS activity increased only when exercise intensity exceeded Tvent.     

Heart rate variability and body temperature during the sleep onset period

Heart rate variability (HRV) and body temperature during the sleep onset period was examined. The core body temperature and electrocardiogram were recorded continuously beginning 1 h before lights out (LO) until the end of the first rapid eye movement sleep (REM) in 14 young healthy subjects. HRV was calculated by the MemCalc method. The time course changes in body temperature and HRV was analyzed before and after sleep onset, and during the following eight consecutive phases: the 60 min before LO, the 30 min before LO, LO, first stage 2 (sleep onset), first slow wave sleep (SWS), stage 2 just before REM, start of REM, and end of REM. A clear decline was observed in the ratio of the low frequency (LF) to high frequency (HF) component of HRV (LF/HF), normalized LF (LF/(LF + HF)), and body temperature prior to sleep onset both in the time course of the sleep onset period and in the consecutive phases. The HF increased prior to sleep onset in the consecutive phases, while no clear increase was observed in the time course of sleep onset period. Changes in LF/(LF + HF) and LF/HF preceded SWS and REM. These results suggest the existence of a strong coupling between the cardiac autonomic nervous system and body temperature at the sleep onset period that may not be circadian effects. Furthermore, LF/(LF + HF) and LF/HF may possibly anticipate sleep and the onset of each sleep stage.

     

Frequency characteristics of long-term heart rate variability during constant-routine protocol

The effects of such behavioral factors as physical activity, food intake, and circadian rhythm on long-term heart rate variability (HRV) in humans remain poorly understood. We therefore studied their effects on HRV using a constant-routine protocol that included simultaneous core body temperature (CBT) correction. Seven healthy subjects completed the constant-routine and daily-routine protocols, during which HRV and CBT were continuously monitored. During the constant routine, subjects were kept awake for 27 h in a semirecumbent posture with minimal physical activity; small isocaloric meals were provided every 2 h. During the daily routine, subjects carried on their lives normally. Data were analyzed using generic spectral analysis based on a fast Fourier transform; coarse-graining spectral analysis was also used to eliminate periodicity due to the regular meals for raw HRV and for the CBT-corrected HRV without circadian and/or low-frequency ultradian components. The results showed that 1) the power spectra of HRV in the constant routine and daily routine had similar power-law scalings at frequencies above approximately 10(-3.5) Hz, while 2) below that crossover frequency, HRV was smaller in the constant routine than in the daily routine, with the difference becoming significant (P < 0.05) at <10(-4) Hz, 3) coarse-graining spectral analysis eliminated diet-induced peaks in generic spectral analysis-based HRV spectra during the constant routine and emphasized the crossover at approximately 10(-3.5) Hz, and 4) CBT correction did not alter the results. Below a frequency of approximately 10(-3.5) Hz (a period >1 h), HRV is strongly influenced by behavioral factors; above that crossover frequency, HRV is behavior independent, possibly reflecting an intrinsic regulatory system.     

Cortisol release and heart rate variability in horses during road transport

Based on plasma cortisol concentrations it is widely accepted that transport is stressful to horses. So far, cortisol release during transport has not been evaluated in depth by non-invasive techniques such as analysis of salivary cortisol and faecal cortisol metabolites. Transport also causes changes in heart rate and heart rate variability (HRV). In this study, salivary cortisol, faecal cortisol metabolites, heart rate and HRV in horses transported by road for short (one and 3.5 h) and medium duration (8 h) were determined. With the onset of transport, salivary cortisol increased immediately but highest concentrations were measured towards the end of transport (4.1 ± 1.6, 4.5 ± 2.6, 6.5 ± 1.8 ng/ml in horses transported for one, 3.5 and 8 h, respectively). Faecal cortisol metabolite concentrations did not change during transport, but 1 day after transport for 3.5 and 8 h had increased significantly (p < 0.01), reflecting intestinal passage time. Compared to salivary cortisol, changes in faecal cortisol metabolites were less pronounced. Heart rate increased and beat-to-beat (RR) interval decreased (p < 0.05) with the onset of transport. Standard deviation of heart rate increased while root mean square of successive RR differences (RMSSD) decreased in horses transported for 3.5 (from 74 ± 5 to 45 ± 6 ms) and 8 h (from 89.7 ± 7 to 59 ± 7 ms), indicating a reduction in vagal tone. In conclusion, transport of horses over short and medium distances leads to increased cortisol release and changes in heart rate and HRV indicative of stress. The degree of these changes is related to the duration of transport. Salivary cortisol is a sensitive parameter to detect transient changes in cortisol release.     

Nonlinear heart rate variability measures under electromagnetic fields produced by GSM cellular phones

This study was designed to assess the nonlinear dynamics of heart rate variability (HRV) during exposure to low-intensity EMFs. Twenty-six healthy young volunteers were subjected to a rest-to-stand protocol to evaluate autonomic nervous system in quiet condition (rest, vagal prevalence) and after a sympathetic activation (stand). The procedure was conducted twice in a double-blind design: once with a genuine EMFs exposure (GSM cellular phone at 900 MHz, 2 W) and once with a sham exposure (at least 24 h apart). During each session, three-lead electrocardiograms were recorded and RR series extracted off-line. The RR series were analyzed by nonlinear deterministic techniques in every phase of the protocol and during the different exposures. The analysis of the data shows there was no statistically significant effect due to GSM exposure on the nonlinear dynamics of HRV.     

Changes in heart rate variability of athletes during a training camp.

Heart rate variability (HRV) reflects regulatory processes of the cardiovascular system and reveals fractal characteristics. In this paper we investigated standard HRV parameters and scaling characteristics in ten athletes before, during, and after a 2-week training camp to assess the effects of short-term overtraining on cardiovascular control. High-resolution ECGs were recorded over 30 min under resting conditions 1 week before the training camp, after 1 week of training in the camp, and after 3-4 days of recovery. Standard HRV analysis was performed according to Task Force recommendations. Scaling characteristics were assessed, applying detrended fluctuation analysis (DFA). Standard HRV analysis showed significant changes in meanNN and rmssd during the training camp. DFA revealed three distinct regions of scale-invariance and significant alterations during the training camp. In conclusion, HRV might be used to monitor the training state in athletes.     

Spectral analysis of heart rate variability during constant and pseudorandom exercise

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

Elevated variance in heart rate during slow-wave sleep after late-night physical activity

This study investigates the effect of mild physical activity before bedtime on the sleep pattern and heart rate during the night. Nine healthy subjects underwent a habituation night, a reference night, and a physical induction night. The physical induction night did not alter the sleep pattern. Physical activity before bedtime resulted in higher heart rate variance during slow-wave sleep. The low-frequency/high-frequency component (LF/HF) ratio during slow-wave sleep in the physical induction night was significantly higher than during the reference night. Increased mean heart rate and higher LF/HF ratio are related to decreased parasympathetic dominance. Exercise up to 1 h before bedtime thus seems to modify the quality of sleep.     

Mathematical model of heart rate variability

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

Respiratory-related heart rate variability in progressive experimental heart failure

Heart failure is associated with autonomic imbalance, and this can be evaluated by a spectral analysis of heart rate variability. However, the time course of low-frequency (LF) and high-frequency (HF) heart rate variability changes, and their functional correlates during progression of the disease are not exactly known. Progressive heart failure was induced in 16 beagle dogs over a 7-wk period by rapid ventricular pacing. Spectral analysis of heart rate variability and respiration, echocardiography, hemodynamic measurements, plasma atrial natriuretic factor, and norepinephrine was obtained at baseline and every week, 30 min after pacing interruption. Progressive heart failure increased heart rate (from 91 +/- 4 to 136 +/- 5 beats/min; P < 0.001) and decreased absolute and normalized (percentage of total power) HF variability from week 1 and 2, respectively (P < 0.01). Absolute LF variability did not change during the study until it disappeared in two dogs at week 7 (P < 0.05). Normalized LF variability increased in moderate heart failure (P < 0.01), leading to an increased LF-to-HF ratio (P < 0.05), but decreased in severe heart failure (P < 0.044; week 7 vs. week 5). Stepwise regression analysis revealed that among heart rate variables, absolute HF variability was closely associated with wedge pressure, right atrial and pulmonary arterial pressure, left ventricular ejection fraction and volume, ratio of maximal velocity of early (E) and atrial (A) mitral flow waves, left atrial diameter, plasma norepinephrine, and atrial natriuretic peptide (0.45 < r < 0.65, all P < 0.001). In tachycardia-induced heart failure, absolute HF heart rate variability is a more reliable indicator of cardiac dysfunction and neurohumoral activation than LF heart rate variability.