Gerard B.A. Stoelinga

Heart Rate Variability

Heart rate variability, that is, the amount of heart rate fluctuations around the mean heart rate, can be used as a mirror of the cardiorespiratory control system. It is a valuable tool to investigate the sympathetic and parasympathetic function of the autonomic nervous system. The most important application of heart rate variability analysis is the surveillance of postinfarction and diabetic patients. Heart rate variability gives information about the sympathetic-parasympathetic autonomic balance and thus about the risk for sudden cardiac death in these patients. Heart rate variability measurements are easy to perform, are noninvasive, and have good reproducibility if used under standardized conditions [1, 2]. Standardized conditions are necessary because heart rate variability is influenced by factors such as respiratory rate and posture. Increasing age is associated with lower heart rate variability, which is comparable to its influence on the classical autonomic function tests [3]. In our overview, we provide a succinct description of these physiologic influences on heart rate variability as well as of methods to measure heart rate variability. The influences of cardiovascular and neurologic disorders on heart rate variability are described in greater detail. During a 4-year period, all new papers concerning the clinical applicability of heart rate variability in fetal, neonatal, and adult medicine were collected (with the help of Current Contents, ISI, Philadelphia). For this review we selected papers from this collection and, if necessary, gathered less recent but relevant papers. Physiology of Heart Rate Variability Because of continuous changes in the sympathetic-parasympathetic balance, the sinus rhythm exhibits fluctuations around the mean heart rate. Frequent small adjustments in heart rate are made by cardiovascular control mechanisms (Figure 1). This results in periodic fluctuations in heart rate. The main periodic fluctuations found are respiratory sinus arrhythmia and baroreflex-related and thermoregulation-related heart rate variability [4, 5]. Figure 1. Scheme of the cardiovascular control mechanisms responsible for the main periodic fluctuations in heart rate. Due to inspiratory inhibition of the vagal tone, the heart rate shows fluctuations with a frequency equal to the respiratory rate [6]. The inspiratory inhibition is evoked primarily by central irradiation of impulses from the medullary respiratory to the cardiovascular center. In addition, peripheral reflexes due to hemodynamic changes and thoracic stretch receptors contribute to respiratory sinus arrhythmia [4]. Fluctuations with the same frequency occur in blood pressure and are known as Traube-Hering waves [7]. Respiratory sinus arrhythmia can be abolished by atropine or vagotomy [4, 8] and is parasympathetically mediated. The so-called 10-second rhythm in heart rate originates from self-oscillation in the vasomotor part of the baroreflex loop. These intrinsic oscillations result from the negative feedback in the baroreflex [9] and are accompanied by synchronous fluctuations in blood pressure (Mayer waves) [7]. The frequency of the fluctuations is determined by the time delay of the system. They are augmented when sympathetic tone is increased [10-12] and they decrease with sympathetic or parasympathetic blockade [4, 12]. Peripheral vascular resistance exhibits intrinsic oscillations with a low frequency [13, 14]. These oscillations can be influenced by thermal skin stimulation [15] and are thought to arise from thermoregulatory peripheral blood flow adjustments. The fluctuations in peripheral vascular resistance are accompanied by fluctuations with the same frequency in blood pressure and heart rate [15] and are mediated by the sympathetic nervous system. Heart Rate Variability Measurement Heart rate variability can be assessed in two ways: by calculation of indices [16] based on statistical operations on R-R intervals (time domain analysis) or by spectral (frequency domain) analysis of an array of R-R intervals [4]. Both methods require accurate timing of R waves. The analysis can be performed on short electrocardiogram (ECG) segments (lasting from 0.5 to 5 minutes) or on 24-hour ECG recordings. Time Domain Analysis Two types of heart rate variability indices are distinguished in time domain analysis (Figure 2, top). Beat-to-beat or short-term variability (STV) indices represent fast changes in heart rate. Long-term variability (LTV) indices are slower fluctuations (fewer than 6 per minute). Both types of indices are calculated from the R-R intervals occurring in a chosen time window (usually between 0.5 and 5 minutes). An example of a simple STV index is the standard deviation (SD) of beat-to-beat R-R interval differences within the time window. Examples of LTV indices are the SD of all the R-R intervals, or the difference between the maximum and minimum R-R interval length, within the window. With calculated heart rate variability indices, respiratory sinus arrhythmia contributes to STV, and baroreflex- and thermoregulation-related heart rate variability contribute to LTV. Figure 2. Example of an adult heart rate trace. Top. Bottom. Twenty-four-hour ECG recordings are frequently used by cardiologists to calculate heart rate variability. For instance, the SD of all R-R intervals within the 24-hour recording, or the mean of the SD of R-R intervals within successive 5-minute periods, is calculated [17-19] (Table 1). These 24-hour indices of heart rate variability also encounter ultradian rhythms (that is, with a period length greater than 1 hour) in heart rate. Table 1. Heart Rate Variability as a Marker of Prognosis after Myocardial Infarction* Frequency Domain Analysis Since spectral analysis was introduced as a method to study heart rate variability [5, 20], an increasing number of investigators prefer this method to that of calculation of heart rate variability indices Figure 2, bottom). The main advantage of spectral analysis of signals is the possibility to study their frequency-specific oscillations [7, 21, 22]. Thus not only the amount of variability but also the oscillation frequency (number of heart rate fluctuations per second) can be obtained. Spectral analysis involves decomposing the series of sequential R-R intervals into a sum of sinusoidal functions of different amplitudes and frequencies by the Fourier transform algorithm. The result can be displayed (power spectrum) with the magnitude of variability as a function of frequency [23]. Thus the power spectrum reflects the amplitude of the heart rate fluctuations present at different oscillation frequencies (see Figure 2, bottom). Spectral analysis can be performed on a short-lasting heart rate trace of 0.5 minute to several minutes. The individual R-R intervals are obtained by R-wave detection. The subsequent array of R-R intervals must be free of artifacts (for example, missed or spurious R waves). To perform a Fourier function on a time-limited signal, the signal must be periodic and stationary [7]. The series of time intervals between consecutive R waves can be treated as if these intervals are equally spaced (a function of R-R interval length against R-R interval number). The Fourier transformation will then result in a spectrum with power as a function of frequency expressed in cycles per beat. The expression can be transformed in Hertz by dividing by mean R-R interval length. To obtain a real data sequence of events equally spaced in time, the sequential R-R intervals are considered as a function of time, interpolated, and subsequently sampled. To obtain a stationary signal, a detrending procedure must be performed. This can be done by subtracting a least-square polynomial approximation from the original signal or by high-pass filtering [7]. Respiratory sinus arrhythmia gives a spectral peak around the respiratory frequency, the baroreflex-related heart rate fluctuations are found as a spectral peak around 0.1 Hz in adults [4], and the thermoregulation-related fluctuations are found as a peak below 0.05 Hz (see Figure 2, bottom). Measurement Conditions Heart rate variability can be studied under spontaneous conditions or with provocation; for example, standing or head-up tilt (increase in sympathetic tone) or deep breathing at a rate of 6 breaths per minute (increase in respiration-related heart rate variability). A 24-hour Holter ECG recording is part of the routine investigations following an acute myocardial infarction. In most of the studies concerning postinfarction patients, therefore, heart rate variability has been established using these 24-hour ECG recordings. In other fields of medicine, for example, regarding diabetic autonomic neuropathy, short-lasting ECG records (ranging from 0.5 to 10 minutes) have been used to calculate spectral and nonspectral heart rate variability indices. These short-lasting measurements were nearly always performed under standardized conditions with and without autonomic nervous system stimulation (that is, tilt and deep breathing). Commercially Available Equipment Commercial devices to assess 24-hour heart rate variability are now available. The conventional tape recorders for Holter monitoring may show variations in tape speed that may cause erroneous STV results [24]. Therefore speed control is necessary with the help of a timing track, that is, simultaneously recorded, crystal-generated timing pulses. The only study that we know of that evaluates commercially available heart rate variability equipment is the study of Molgaard and colleagues [24] concerning the Pathfinder II system. This system corrects for tape speed errors and has a high accuracy of QRS detection but contains no correction for artificially long R-R intervals [24]. The effect of artificially long R-R intervals depends on the heart rate variability index used. Maturational and Physiologic Influences on Heart Rate Variability Maturity of the Autonomic Ne

Doppler flow characteristics in the main pulmonary artery and the\(\overline {LA} /Ao\) ratio before and after ductal closure in healthy newborns/Ao ratio before and after ductal closure in healthy newborns

SummaryEcho-Doppler (ED) techniques were used to estimate the time of closure of the ductus arteriosus in 30 normal neonates. We found that after birth there was a left-to-right (L-R) shunt through the ductus, which disappeared within 14 hours in 50% of the neonates investigated. Furthermore, patency of the ductus was not associated with a murmur. After closure of the ductus there was a significant diminution of the echocardiographically determined left atrium/aortic (

The influence of respiratory distress syndrome on heart rate variability in very preterm infants

\overline {LA} /Ao

Cerebral blood flow velocity and pulsation in neonatal respiratory distress syndrome and periventricular hemorrhage

) ratio, which was used as a measure of the L-R shunt.

Asymmetry of the cerebral blood flow: an ultrasound Doppler study in preterm newborns

ABSTRACT: To study the influence of artificial ventilation rate on neonatal heart rate variability (HRV), ECG and respiratory impedance curves were recorded four times a day in 20 preterm infants (<33 wk) during the first 3 d after birth while the infants were ventilated at a wide range of ventilator rates. The contents of selected frequency bands within the R-R interval power spectrum were calculated for 3-min periods. Respiratory distress syndrome severity was assessed at each measurement. Respiratory sinus arrhythmia (RSA) induced by the ventilator appeared to mimic spontaneous RSA. As in spontaneous respiration, the amount of RSA (power in a frequency band around the respiratory rate) increases as the ventilation rate decreases. This phenomenon is most probably due to entrainment with baroreflex-related fluctuations in the heart rate. Although the artificial ventilation rate influences RSA and thus high-frequency HRV, an increase in respiratory distress syndrome severity results in a decrease in low-frequency HRV. Thus, the attenuation of low-frequency HRV by respiratory distress syndrome is not likely to be due to artificial ventilation.

The influence of physiological parameters on long term heart rate variability in healthy preterm infants

In a multi-parametric study the influence of pathological neonatal conditions on heart rate variability was investigated in 60 preterm infants born at a gestational age below 33 weeks. Measurements were performed during the first 3 days of life. Four times a day, RR-intervals, respiration curve and rate, transcutaneously measured blood gases and observed body movements were recorded while the infants were asleep. All data were stored simultaneously in a micro-computer. Severity of respiratory distress syndrome (RDS), patency of ductus arteriosus and periventricular haemorrhage were documented as well. Four sets of short- (STV) and long-term variability (LTV) indices were calculated. Severe RDS was associated with a significant decrease in LTV. The influence of RDS on LTV persisted after correction for conceptional age, postnatal age, behavioural state and variations in respiratory rate and in transcutaneous PO2. Infants with a symptomatic patent ductus arteriosus had lower LTV than controls with the same severity of RDS. STV was predominantly influenced by postnatal and conceptional age, and tended to be lower in infants with periventricular haemorrhage.

Ultrasound detection of congenital Arteriovenous aneurysm of the great cerebral vein of Galen

The relationship of cerebral blood flow fluctuation (CBFF) with periventricular haemorrhage (PVH) and respiratory distress syndrome (RDS) was studied in 35 preterm newborns. CBFF was defined as the interquartile range in the ensemble of pulses of a 20-s Doppler recording of CBF velocity (CBFV) in the internal carotid artery. We found a statistically significant increase in end diastolic CBFF in PVH and RDS. This increase was related to the mode of respiration (spontaneous or mechanically supported), the state of the ductus arteriosus, and the level of end diastolic CBFV. Differences before and after the onset of PVH were not found. In view of this, we conclude that RDS increases CBFF, that this increase is related to pleural pressure fluctuations, that these can be damped by mechanical ventilation, and that their propagation to the CBF is promoted by patency of the ductus arteriosus and foramen ovale. Whether the CBFF increase causes PVH, or is merely an expression of coincident RDS, remains a question that needs further investigation.

Influence of end expiratory pressure on cerebral blood flow in preterm infants

The present study addressed the hypotheses that cerebral ischemia and/or excessive cerebral blood pulsation contribute to periventricular hemorrhage in preterm newborns with respiratory distress and that the pulse width is a valuable tool to estimate the contribution of cerebral blood pulsation. These hypotheses were tested by following preterm newborns at risk for respiratory distress and periventricular hemorrhage. We monitored for cerebral blood flow velocity (CBFV), cerebral pulse width, and cerebral pulsatility index; for patent ductus arteriosus, capillary Pco2, heart rate (HR) and behavior; and for the occurrence of respiratory distress and periventricular hemorrhage (PVH). The data obtained were analyzed with linear regression with the mode of respiration (spontaneous or supported) and postnatal age as additional covariates. We observed that (a) respiratory distress, either uncomplicated or complicated by PVH, correlates with a low CBFV and a high cerebral pulsatility index; (b) PVH also correlates with a high cerebral pulse width; (c) the increased pulse width precedes the onset of the hemorrhage; and (d) these CBF alterations can be partly attributed to ductal shunting and are ameliorated by mechanical ventilation.