Hidden Rhythms in the Heart Rate Record: a Primer on Neurocardiology

Return to December 2000Table of Contents

BEDSIDE PHYSIOLOGY Hidden rhythms in the heart rate record: a primer on neurocardiology

Ernest L. Fallen, MD Dr. Fallen is Professor of Medicine with the Division of Cardiology, Department of Medicine, McMaster Univer- sity, Hamilton, Ont.

Medical subject headings: cardiovascular physiology; Cheyne- Stokes respiration; electrocardiography; Fourier analysis; heart rate; neurotransmitters; nonlinear dynamics; syncope, vasovagal

Clin Invest Med 2000;23(6):387-94.

Introduction fying loss of physiologic regulation. Simply stated, the more variable are successive interbeat intervals Although we appear to think and act in the time do- the healthier is the person. How do we know this? main events, the neurobiologic processes that govern our existence are played out in the frequency do- Physiologic correlates of heart rate variability main.* In other words, our brain is constantly emit- ting signals that oscillate at varying frequencies. The Contained within any heart rate record (ECG) are frequency content of these neural rhythms reveals hidden rhythms that reflect autonomic regulation of marked variability even under steady state conditions sinus node function.1Ð3 Before describing these in healthy people. It is only when these oscillations rhythms, it should be recalled that the sinoatrial node begin to lose their variability (i.e., become more peri- is the target of competing bombardment by both odic), that central control of individual organ func- sympathetic and vagal efferent nerve impulses. The tion is endangered. Consider the following bedside resultant interbeat interval (the inverse of heart rate) observations: the patient with end-stage heart or lung at any point in time is simply the net effect, or bal- disease whose breathing pattern alternates between ance, between vagal neurotransmission (acetyl- hyperpnea and apnea (Cheyne-Stokes respiration); choline) and sympathetic neurotransmission (norepi- the patient with advanced biventricular failure whose nephrine) at the neuroeffector junction. The arterial pulse volume waxes and wanes with succes- competition occurs at both ends. At the sinus node sive beats (pulsus alternans); or the patient with criti- site, there is what is called accentuated antagonism cal coronary artery stenosis whose T-wave morphol- between release of the neurotransmitters. This means ogy on the electrocardiogram (ECG) begins to that the strength (amount) of neurotransmitter re- alternate from beat to beat prior to ventricular fibrilla- lease from either a vagal or a sympathetic stimulus is tion. In all 3 instances, the common denominator is a highest when the opposing agonist is already domi- shift from complex variability of the beat-to-beat os- nant.4 In any event, the release of either neurotrans- cillatory rhythm to a periodicity akin to a simple sine mitter is said to be proportional to so-called auto- wave, a dangerous pattern signi- nomic tone (i.e., the rate of nerve impulses as viewed along the course of an efferent nerve in the time domain). To understand what is happening at *The time domain refers to events in unit time. The frequency domain refers to the combination of frequencies that underlie the central site it is important to distinguish between events such as the beat-to-beat heart rate variability. tonic and phasic properties of nerve conduction be-

Clin Invest Med ¥ Vol 23, no 6, December 2000 387 Fallen

cause we are more concerned with heart rate as a sympathetic and vagal input to all or any portion of measure over time than with “instantaneous” heart the signal.5 rate, an implausible term. Frequency domain measures such as the power If we examine a recording of nerve impulses we spectrum density of HRV provide information on would first see a train of repetitive bursts of electrical how the variance or power in the heart rate signal spikes. This is often referred to as its tonic activity. distributes as a function of time.3 Methods include On more careful inspection an oscillatory or phasic the nonparametric fast Fourier transform1 (e.g., pattern to the signal can be discerned, a property indi- BlackmanÐTukey method) and the parametric autore- cating that the train of nerve impulses has a fre- gressive model technique.2 The fundamental principle quency modulation not dissimilar to that of an FM is based on a conversion or demodulation of a time radio signal. If we then visualize a succession of R series such as a continuous series of RÐR interval in waves in a continuous ECG record as containing spe- the time domain (a tachygram) into its frequency cific oscillatory or phasic patterns we can dial in the components (Fig. 1). What first appears as a random various FM bands that make up the power spectrum.3 series of disconnected R-R spikes on the time axis is It turns out that these hidden phasic rhythms re- miraculously transformed into a smooth spectrum flect autonomic control of sinoatrial function.1,2 By with 2 distinct peaks. The low frequency band (0.05 applying mathematical algorithms using standard to 0.15 Hz) represents primarily sympathetic modula- signal-processing techniques we can easily tease out tion in the resting supine state,7 whereas the high fre- and separate the relative influences of sympathetic quency band (0.15 to 0.35 Hz) is essentially respira- and vagal modulation of heart rate and blood pres- tory driven sinus arrhythmia, a manifestation of vagal sure variability.5 modulation, as this band is completely abolished with high dose atropine.1 Hence, the ratio of the low fre- Methodologies quency to high frequency power provides an expression of sympathovagal balance at any steady state The 2 principal approaches for measuring heart rate junction in time. The major advantage of the fre- variability (HRV) are by time domain and frequency quency domain approach, therefore, is to provide a domain analyses. In the time domain method, simple window through which there is a better view of phys- descriptive statistics are used to obtain a measure of iologic control of sinoatrial function compared with HRV from many interbeat intervals.6 For example, time domain measures. Its principal disadvantage is from 24 hours of a continuous ECG recording the requirement for strict steady state conditions to (Holter monitoring) the standard deviation of all suc- obtain, making it valid for short-term recordings (2.5 cessive conducted RÐR intervals originating in the to 5 minutes) only.5 There are other more complex sinus node can be derived; the SDNN. These de- methods, including nonlinear analyses such as the scriptive statistics mainly capture the overall vari- various methods based on chaos theory8 and time ance or total power contained within the 24-hour in- variant techniques.9,10 The latter permit noise-free terbeat signal. There are other time domain measurements during physiologic perturbations. To measures, known as differencing parameters, that date, the clinical utility of these intriguing nonlinear mainly reflect vagal activity. These include the root methods awaits further testing and application. mean square standard deviation of successive R-R interval differences and the proportion of successive A word on clinical utility interbeat intervals that vary more than 50 milliseconds. The advantages of time domain methods are It has been shown that a patient recovering from an their simplicity, reproducibility and proven prognos- acute myocardial infarction (MI) with an SDNN less tic power in certain clinical disorders (vida infra). than 50 milliseconds has a fourfold increase in the Their disadvantages include non-steady state of the risk of sudden death within 1 year compared with signal in the free living ambulatory setting and the the patient whose SDNN is greater than 100 mil- difficulty in ascertaining the relative contributions of liseconds (Fig. 2). Thus, a low HRV, whether mea-

388 Clin Invest Med ¥ Vol 23, no 6, décembre 2000 Neurocardiac regulatory mechanisms

sured in the time or frequency domain, stratifies the established risk factors in post-MI patients. Yet, post-MI patient into a group at high risk for malig- whereas HRV has reasonable prognostic power, its nant arrhythmias.11,12 This information is not only accuracy as a screening tool is weak if employed as prognostic but offers incremental value with other a single test. With regard to sudden cardiac death,

Fig. 1: Steps in the derivation of the power spectrum of heart rate (HR) variability. The R-R interval of the electrocardiogram (ECG) is transformed into a tachygram (instantaneous HR series) from which the frequency components are computed, yielding the power spectrum by either the Blackman–Tukey (fast Fourier transform) or the smoother autoregressive modelling technique.

Clin Invest Med ¥ Vol 23, no 6, December 2000 389 Fallen

there is a wealth of laboratory evidence supporting distinct spectral components of the heart rate signal the association of autonomic dysregulation with a indicating at least partial reinnervation.15,16 To what predisposition to lethal arrhythmias.13,14 A few at- extent early reinnervation is clinically beneficial re- tempts have been made (without success so far) to mains to be explored, but at least we have a simple forecast the onset of sustained ventricular tachycar- noninvasive method by which to monitor these pa- dia or fibrillation by spot-checking for a specific tients. In patients with diabetic autonomic neuropathy change in HRV before the event. One confounding there is, as with heart failure, a significant reduction variable is the progressive age-dependent decline in of HRV.17 Moreover, there is a failure to increase the HRV in healthy people, creating a greater and more low-frequency power during orthostasis, signifying widespread data overlap in the elderly. Another limi- severe impairment of baroreceptor sensitivity. HRV tation is the poor signal-to-background noise ratio can be modified by a variety of therapeutic interven- noted in some disease states such as advanced con- tions. Those that either decrease the low-frequency gestive heart failure and diabetes mellitus. (sympathetic) power or increase the high-frequency It was once believed that orthotopic heart trans- (vagal) power, or both, include β blockade, exercise plantation resulted in permanent denervation of the training, low-dose atropine derivatives and an- donor heart. However, several studies have revealed giotensin converting enzyme inhibitors to name a few.3 However, it has yet to be established that in- creasing the variance of the heart rate signal by itself is effective in preventing serious clinical outcome events. So, except for its predictive power, useful clinical applications for HRV must await either a refinement in technique or a breakthrough in our un- derstanding of autonomic physiology. In this regard it is more interesting to speculate on the potential utility of these noninvasive methods by engaging in some challenging perspectives in theoretic physiology.

Concept of negative feedback

Activation of cardiac and vascular vagoafferent receptors is fundamentally inhibitory to central sympathetic neural discharge.18 For example, vagoafferent impulses, whether arising from high pressure arterial baroreceptors in the carotid sinus or unmyelinated C fibres in the ventricular myocardium, elicit a negative feedback check on an otherwise unfettered feed for- ward sympathoexcitatory state. Central integration and processing take place in several midbrain re- Fig 2: Four-year survival after myocardial infarction based on the 24-hour standard deviation of normal- gions, converting or transducing ascending tonic im- to-normal beats (SDNN) (ms) taken within 1 to 2 pulses into phasic or modulatory efferent output to weeks of the acute event. Note the difference in the heart and vascular structures. Examples of this mortality (almost 40%) for patients having an nonlinear negative feedback effect include low- SDNN < 50 ms compared with those having an pressure baroreceptors in the right heart and pul- SDNN > 100 ms (reproduced with permission of monary venoatrial junctions that are integral to vol- Elsevier Science from Kleiger RE, Miller JP, Bigger JT, Moss AJ. Decreased heart rate variability and its ume control; arterial and ventricular high-pressure association with increased mortality after acute my- baroreceptors that mediate short-term adaptations in ocardial infarction. Am J Cardiol 1987;59:259). blood pressure control; respiratory driven augmenta-

390 Clin Invest Med ¥ Vol 23, no 6, décembre 2000 Neurocardiac regulatory mechanisms

tion of HRV and the antinociceptive effect of afferent syncope occurring as a result of excessive activation vagal signals on sympathetic efferent activity.19 Clini- of right ventricular mechanoreceptors. cally, these receptors and their central inhibitory pro- Although the power spectrum of HRV is a linear jections play a profound role in many disorders such construct mathematically, the frequency composition as the bradycardia and hypotension seen with activa- of the beat-to-beat heart rate series (and blood pres- tion of posterior wall vagoafferent C fibres during an sure series) can be viewed as the phasic expression acute inferoposterior MI, the effort-induced syncope of central integrative responses to a variety of tonic precipitated by activation of the same ventricular input signals arising from many different sites. baroreceptors in tight aortic stenosis or vasodepressor These competing input signals influence the power

Fig. 3: Cerebral evoked amplitude (N2/P2) and heart rate autospectral (LF:HF) responses to varying intensities of esophageal electrical (vagoafferent) stimulation. Note the linear correlation between N2/P2 amplitude and stimulus intensity compared with the flat nonlinear response of the LF:HF ratio. Circles = LF:HF area, squares = N2/P2 peak. *p < 0.05, #p < 0.005 (reproduced with permission of Elsevier Science from Kamath MV, Hollerbach S, Bajwa A, Fallen EL, Upton AR, Tougas G. Neurocardiac and cerebral responses evoked by esophageal vago-afferent stimulation in humans: effect of varying intensities. Cardiovasc Res 1998;40:595).

Clin Invest Med ¥ Vol 23, no 6, December 2000 391 Fallen

spectrum through mechanisms that range from selec- brain.20 This leads logically into a consideration of tive frequency entrainment to changes in the gain of nonlinear dynamic systems of which deterministic feedback loops such as the slope of baroreceptor chaos is an example. sensitivity. The critical balance between sympathetic and vagal control of cardiovascular function is there- Concept of nonlinearity (deterministic chaos) fore largely determined by these phasic or oscillating rhythms, which in turn are determined by the com- A linear system is one in which the magnitude of a plex interplay of numerous neural pathways in the response is proportional to the strength of the stimu-

Fig. 4: Predictability curves (means and standard deviations) calculated by the linear prediction model for young healthy people (panel a), old healthy people (panel b), post-myocardial infarction patients on β block- ers (panel c) and patients with heart failure (panel d). The relative flatness of the curve in panel d suggests some loss of complexity. Solid line = predictability curve, dotted line = mean (SD) of each value of ρ calculated from 10 surrogate time series. ρ = correlation coefficient for predicted and actual values, p = the number of time steps into the future (reproduced from Lefebvre JH, Goodings DA, Kamath MV, Fallen EL. Pre- dictability of normal heart rhythms and deterministic chaos. Chaos 1993;3:267-76).

392 Clin Invest Med ¥ Vol 23, no 6, décembre 2000 Neurocardiac regulatory mechanisms

lus. A nonlinear system, does not exhibit proportion- in so far as T-wave alternans, a breakdown of fractal ality. Suppose we were to electrically stimulate a scaling and a loss of entropy, has been implicated as vagoafferent nerve repetitively (i.e., tonically) and a forerunner of malignant ventricular arrhythmias.25 measure the brain’s response, represented by cerebral Galileo is said to have measured the isochronicity evoked potentials, as well as the cardiac efferent re- of his pendulum by timing its oscillation with his sponse as measured by the power spectrum of HRV. own pulse. If this were precisely true then according The amplitude of the cerebral evoked to what we have learned so far he would have been potentials will be proportional to the strength of the dead in seconds! viscerosensory afferent stimulus.21 However, the efferent response, a post-central processing effect, will References behave in a nonlinear fashion.22 In other words, the power contained within low- or high- 1. Akselrod S, Gordon D, Ubel FA, Shannon DC, Berger frequency bands will show a disproportionate re- AC, Cohen RJ. Power spectrum analysis of heart rate sponse to varying the vagoafferent stimulus intensity fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 1981;213(4504):220-2. (Fig. 3). There is a compelling theoretic argument that the 2. Malliani A, Pagani M, Lombardi F, Cerutti S. Cardio- degree of nonlinearity is in part an expression of the vascular neural regulation explored in the frequency system’s complexity, that is, the number of diverse domain. Circulation 1991;84:1482-92. inhibitory feedback loops, all of which are cleverly 3. Kamath MV, Fallen EL. Power spectral analysis of designed to provide balance while facilitating rapid heart rate variability: a noninvasive signature of car- responses to acute perturbations. Think of a rheostat diac autonomic function. Crit Rev Biomed Eng oscillating about a set point offering respectable de- 1993;21: 245-311. grees of freedom. The closer the oscillations approach the set or equilibrium point the more faulty 4. Levy MN, Schwartz PJ, editors. Vagal control of the heart: experimental basis and clinical implications. the mechanism. The degree of biologic complexity Armonk (NY): Futura; 1994. in any system is potentially measurable by a bewil- dering array of different nonlinear methods, includ- 5. Task Force of the European Society of Cardiology and ing fractals, bifurcations, detrending analyses, ap- the North American Society of Pacing and Electro- proximate entropy and predictability models to name physiology. Heart rate variability: standards of mea- 8 surement, physiological interpretation and clinical use. a few. Whatever the method, there is hope that these Circulation 1996;93(5):1043-65. algorithms are shedding light on the complexity within dynamic systems. Why is this important? 6. Stein PK, Bosner MS, Kleiger RE, Conger BM. Heart I began by citing certain disease states in which rate variability: a measure of cardiac autonomic tone. simple periodic rhythms such as Cheyne-Stokes res- Am Heart J 1994;127:1376-81. piration or pulsus alternans, easily observed at the 7. Montano N, Ruscone TG, Porta A, Lombardi F, Pa- bedside, foretold a guarded prognosis for these pagani M, Malliani A. Power spectrum analysis of heart tients. Goldberger23 has referred to these periodic rate variability to assess the changes in sympathovagal rhythms as “decomplexification,” which character- balance during graded orthostatic tilt. Circulation izes the end-stages of many disease states. He stated, 1994;90:1826-31. “When physiologic systems become less complex 8. Goldberger AL, Rigney DR, West BJ. Chaos and frac- their information content is degraded. As a result tals in human physiology. Sci Am 1990;262:42-9. they are less adaptable and less able to cope with the exigencies of a constantly changing environment.” 9. Bianchi A, Mainardi L, Patrucci E. Time variant spec- So far most of these applications are in the theoretic tral estimation of heart rate variability signal. IEEE Trans Biomed Eng 1993;40:136-44. realm and have yet to withstand clinical verification, 24 although some early attempts are being made (Fig. 10. Kamath MV, Fallen EL, McArthur A, Runions J. De- 4). There is also evidence of prognostic verification tection of silent myocardial ischemia during ambula-

Clin Invest Med ¥ Vol 23, no 6, December 2000 393 Fallen

tory monitoring by time frequency power spectral Auton Nerv Syst 1987;19(2):119-25. analysis. Ann Noninv Elect Cardiol 1996;1:63-9. 18. Bishop VS, Hasser EM. Arterial and cardiopulmonary 11. Kleiger RE, Miller JP, Bigger JT, Moss AJ. Decreased reflexes in the regulation of the neurohumoral drive to heart rate variability and its association with increased the circulation [review]. Fed Proc 1985;44(8):2377-81. mortality after acute myocardial infarction. Am J Car- diol 1987;59:256-62. 19. Ren K, Randich A, Gebhart GF. Vagal afferent modulation of spinal nociceptive transmission in the rat. J 12. Bigger JT, Fleiss JL, Steinman RC, Rolnitzky LM, Neurophysiol 1989;62(2):401-15. Kleiger RE, Rottman JN. Frequency domain measures of heart period variability and mortality after myocar- 20. Malliani A. Principles of cardiovascular neural regu- dial infarction. Circulation 1992;85:164-71. lation in health and disease. Hingham (MA): Kluwer Academic Publishers; 2000. 13. Schwartz PJ, Vanoli E, Dtramba-Badiale M, De Fer- rari GM, Billman GE, Foreman RD. Autonomic mech- 21. Hollerbach S, Kamath M, Fitzpatrick D, Shine G, anisms and sudden cardiac death: new insights from Fallen E, Upton AR, et al. The cerebral response to the analysis of baroreceptor reflexes in conscious dogs electrical stimuli in the esophagus is altered by in- with and without a myocardial infarction. Circulation creasing stimulus frequencies. Neurogastroenterol 1988;78:969-79. Motil 1997;9:129-39.

14. Verrier RL. Neurogenic aspects of cardiac arrhythmias. In 22. Kamath MV, Hollerbach S, Bajwa A, Fallen EL, Up- El-Sharif P, Samet N, editors. Cardiac pacing and electro- ton AR, Tougas G. Neurocardiac and cerebral re- physiology. Philadelphia: W.B. Saunders; 1991. p 77. sponses evoked by esophageal vago-afferent stimulation in humans: effect of varying intensities. 15. Sands KE, Appel ML, Lilly LS, Schoen FJ, Mudge Cardiovasc Res 1998;40:591-9. GH Jr, Cohen RJ. Power spectrum analysis of heart rate variability in human cardiac transplant recipients. 23. Goldberger A. Non-linear dynamics for clinicians: Circulation 1989;79(1):76-82. chaos theory, fractals and complexity at the bedside. Lancet 1996;347:1312-4. 16. Fallen EL, Kamath MV, Ghista DN, Fitchett D. Spec- tral analysis of heart rate variability following human 24. Lefebvre JH, Goodings DA, Kamath MV, Fallen EL. heart transplantation: evidence for functional reinner- Predictability of normal heart rhythms and determinis- vation. J Auton Nerv Syst 1988;23:199-202. tic chaos. Chaos 1993;3:267-76.

17. Lishner M, Akselrod S, Avi VM, Oz O, Divon M, 25. Rosenbaum DS, Jackson LE, Smith JM, Garan H, Ravid M. Spectral analysis of heart rate fluctuations. Ruskin JN, Cohen RJ. Electrical alternans and vulnera- A non-invasive, sensitive method for the early diag- bility to ventricular arrhythmia. N Engl J Med 1994; nosis of autonomic neuropathy in diabetes mellitus. J 330:235-41.

394 Clin Invest Med ¥ Vol 23, no 6, décembre 2000