Clinical ReviewHeart rate variability, sleep and sleep disorders
Introduction
Heart rate (HR) is modulated on a beat-to-beat basis by the combined effects of the sympathetic (SNS) and parasympathetic (PNS) nervous systems on the sino-atrial node. Therefore, analysis of changes in HR over time (heart rate variability or HRV) provides information about autonomic functioning. In clinical conditions associated with autonomic dysfunction, (e.g., congestive heart failure, diabetes, end-stage renal disease, etc.), abnormal, usually decreased, HRV is generally found. Moreover, abnormal HRV is an independent risk factor for mortality both in clinical and population studies.1 It should be noted that HRV cannot reflect autonomic “tone” which can only be measured using pharmacological blockade. Moreover, HRV is a black box with HR as the output. Hence the cause of decreased HRV, whether a lack of central signaling, lack of reflex feedback to the central nervous system or lack of responsiveness of the heart itself, cannot be determined.
HRV is generally derived from mathematical analyses of intervals between normal heart beats (NN intervals) and requires patients to be in sinus rhythm for most measures to be meaningful from HRV alone. However, one HRV measure, HR turbulence, is based on the NN interval response to ventricular ectopic beats.
Although millions of continuous polysomnogram (PSG) electronic electrocardiogram (ECG) signals from which HRV could potentially be calculated have been stored, the potential for obtaining additional, clinically relevant information from them has scarcely been tapped. At the same time, there are a huge number of 24-h Holter recordings, and increasingly, multi-day telemetry recordings from which not only HRV, but clinically relevant information about sleep could be derived, yet these important data are generally ignored. Thus, in the current review, we will focus on a basic understanding of HRV and then on potential sleep-related clinical applications of HRV from both PSG ECGs and 24-h ambulatory recordings. For each section, a table will be provided that describes the population and methods for citations.
Section snippets
Measurement of HRV
The starting point for HRV analysis is a list (a “beat file”) of the intervals in milliseconds between heart beats on the ECG recording that includes the morphology of each heart beat so that normal, ectopic, paced beats and artifact can immediately be identified. Although beat files (or RR interval files) can be exported from many PSGs, they usually do not have morphology annotations. Thus generating an accurate beat file generally requires some form of Holter scanning, just as PSG software
HRV during different sleep stages (Table 3)
One sleep-related application of HRV explores changing autonomic function during different stages of sleep. Zemaityte et al. studied HR and HRV changes by sleep stage in healthy young adult males.9 HR decreased in association with decreased variability in sleep stages 1, 2, 3 and 4, whereas HR increased, with increased variability, in REM sleep. HR during REM sleep was higher than during wakefulness. The high frequency peak was pronounced during slow-wave sleep, and was abolished with
Acute HR and HRV effect of Sleep Apnea (Table 4)
With preserved cardiac autonomic function, sleep-disordered breathing (SDB) induces dramatic changes in HR and in hemodynamics.18, 19 These result in an HR pattern termed cyclic variation of HR (CVHR), clearly seen when instantaneous HR is graphed on an appropriate scale.20 CVHR peaks are due to abrupt increases in HR, sometimes as much as 30 bpm, during the arousal phase that terminates SDB events. Fig. 2a shows CVHR on an HR tachogram for a patient with severe OSA and Fig. 2b shows the
Chronic HRV effect of sleep-disordered breathing (Table 5)
The effect of OSA on daytime HRV was studied by Hilton et al. in uncomplicated patients vs. matched healthy controls.25 HRV was calculated on 25-min of data during wake to assess awake ANS function. HF% was significantly (p < 0.03) reduced in OSA compared to controls. In the previously cited study,26 where daytime peripheral SNS activity was significantly increased in uncomplicated moderate-to-severe OSA patients compared with unmatched control subjects, a significant daytime increase in LF/HF
HRV effect of periodic leg movements (Table 6)
Several investigators have examined HRV in association with periodic limb movements (PLMs). Sforza et al. analyzed the effects of PLMs on HRV during non-REM sleep in patients with PLMs but without other major sleep disorders, neuromuscular or cardiac diseases.32 Standard HRV was calculated during selected 10-min periods. Results were interpreted as showing that the occurrence of PLMS was associated with an increase of SNS activity without significant changes in PNS activity. Winkelman found a
Using HRV to screen for sleep-disordered breathing (Table 7)
Guilleminault et al. first coined the term cyclic variation of the heart rate and demonstrated the feasibility of using it to screen for moderate and severe OSA.36 In the same study, they concluded that CVHR was regulated mainly by vagal instead of SNS activity. This conclusion was based on the observations that CVHR is abolished by IV injection of atropine (vagal pressor) but not by propranolol (non-selective beta blocker). This discovery was not followed up until more recently when advances
HRV effects of insomnia and sleep deprivation (Table 8)
Aside from their sleep complaints, few physiologic measures consistently differentiate between patients with insomnia and those with normal sleep.50 The concept that hyperarousal processes “play a key role in the pathophysiology of primary insomnia” makes it intuitively appealing that HRV might capture some differences in underlying autonomic state during insomnia.51 HRV has been compared between insomniacs and good sleepers in a small number of studies. HRV was calculated from a 5-min segment
Using HRV to monitor the effect of CPAP (Table 9)
Effective CPAP abolishes CVHR, while at the same time markedly reducing the excessive SNS activity due to respiratory events. Therefore, use of CPAP would be expected to have significant effects on both nighttime and daytime autonomic function. Direct measurement of MSNA has confirmed a reduction in SNS activity associated with successful CPAP.22 However, MSNA is not clinically useful for assessment of changes in autonomic function associated with OSA treatment. HRV provides a noninvasive
Cardiopulmonary coupling: a novel application of HRV to sleep (Table 10)
Thomas and his collaborators at Harvard proposed a new approach to measure HRV during sleep called the cardio-pulmonary coupling (CPC) algorithm.76 This algorithm uses short-windowed analyses of the cross-spectrum of 2 ECG-derived signals: RR intervals and a surrogate respiration signal from R-peak amplitude changes or estimated cardiac vector axial changes, assuming both are modulated by respiration. The CPC cross-spectrum, the product of the Fourier transform amplitudes of the 2 derived
Use of HRV during sleep for risk stratification (Table 11)
Although there have been numerous studies of the predictive value for adverse outcomes of HRV assessed over periods ranging from 24 h to a few seconds, little has been done with the enormous number of ECG signals from stored PSGs that could potentially be used to develop criteria for risk stratification using overnight HRV or potentially even HRV changes with different sleep stages. While overnight PSG can be viewed as a short 24-h Holter, there is evidence that sleep per se is a period that may
Conclusions and recommendations
The many studies reviewed here clearly demonstrate the relevance of HRV analysis to clinical sleep medicine. At the same time, it is clear that clinical applications of HRV to sleep are in their infancy, and the naiveté with which HRV numbers are sometimes accepted as measures of autonomic function without examination of the underlying HR patterns must be emphasized. This is a general problem in HRV research, not just in sleep-related applications. Also, although not discussed in detail here,
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