QJM vol. 98 no. 2 © Association of Physicians 2005; all rights reserved.
Review |
Heart rate variability measurements and the prediction of ventricular arrhythmias
From the Department of Emergency Medicine, Royal Infirmary of Edinburgh, Edinburgh, and 1Cardiodigital Ltd, Elvingston Science Centre, Gladsmuir, UK
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Introduction |
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 Top Introduction The history of HRVProblems with measuring HRVMeasuring HRVHRV and the onset...Conclusions and further workReferences |
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Heart rate variability (HRV) is the temporal variation between sequences of consecutive heartbeats. On a standard electrocardiogram (ECG), the maximum upwards deflection of a normal QRS complex is at the peak of the R wave (Figure 1), and the duration between two adjacent R wave peaks is termed the R-R interval. The ECG signal requires editing before HRV analysis can be performed, a process requiring the removal of all non-sinus-node-originating beats. The resulting period between adjacent QRS complexes resulting from sinus node depolarizations is termed the N-N (normal-normal) interval.1 HRV is the measurement of the variability of the N-N intervals.
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Although counter-intuitive, it is possible that HRV confers a survival advantage. Any system exhibiting intrinsic variability is primed to respond rapidly and appropriately to demands placed upon it.
HRV is a measure of the balance between sympathetic mediators of heart rate (HR) (i.e. the effect of epinephrine and norepinephrine, released from sympathetic nerve fibres, acting on the sino-atrial and atrio-ventricular nodes), which increase the rate of cardiac contraction and facilitate conduction at the atrio-ventricular node, and parasympathetic mediators of HR (i.e. the influence of acetylcholine, released by the parasympathetic nerve fibres, acting on the sino-atrial and atrio-ventricular nodes), leading to a decrease in the HR and a slowing of conduction at the atrio-ventricular node. Sympathetic mediators appear to exert their influence over longer time periods and are reflected in the low frequency power (LFP) of the HRV spectrum (between 0.04 Hz and 0.15 Hz.2,3 Vagal mediators exert their influence more quickly on the heart, and principally affect the high frequency power (HFP) of the HRV spectrum (between 0.15 Hz and 0.4 Hz).4 Thus, at any point in time, the LFP:HFP ratio is a proxy for the sympatho-vagal balance.
Physiological and pathological process may influence N-N interval variability. Under normal conditions, the balance between sympathetic and parasympathetic activity favours the latter. Physiological influences may modulate central and peripheral receptor (i.e. carotid sinus) activity. This is demonstrated in the slowing of HR with expiration, and a quickening with inspiration (respiratory sinus arrhythmia). These effects are apparent on the HFP spectrum. Circadian alterations in HRV are present in normal subjects, with higher LFP in the daytime and higher HFP at night.5,6 Exercise, standing and stress in human subjects, and hypotension, and coronary or cerebral ischaemia in dogs, increases sympathetic drive and LFP. Conversely, cold stimulation of the face increases parasympathetic drive and increases HFP.5–7
In normal subjects, a variable heart rate is the normal physiological state. It has been suggested that the healthy heart has a long range ‘memory’ which prevents it from developing extremes of pace, and that this facility erodes as age or disease develops.8 A loss of variability is associated with an increased mortality in patients post myocardial infarction.9 In animal studies, an increase in sympathetic activity can provoke ventricular tachyarrythmias (VTAs)10,11 and lower the ventricular fibrillation threshold.12 This effect is exacerbated by coexistent myocardial ischaemia.13,14 Conversely, vagal activity seems to provide a protective effect against the development of VTAs.15,16 With this exerting the greatest effect on the heart rate in normal conditions and predominantly effecting HFP, a heart rate with much variability is the optimal state most likely to prevent the development of fatal VTAs.
Drug therapy may alter HRV; beta-blocker therapy has been shown to have a favourable effect on HRV17,18 in patients with heart failure. However, changes in HR dynamics observed before VTAs in patients taking anti-arrhythmic drugs were independent of the drug regimen.19
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The history of HRV |
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TopIntroductionThe history of HRVProblems with measuring HRVMeasuring HRVHRV and the onset...Conclusions and further workReferences |
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Heart rate variability (HRV) was first used clinically in 1965 when Hon and Lee20 noted that fetal distress was accompanied by changes in beat-to-beat variation of the fetal heart, even before there was detectable change in the HR. In the 1970s, Ewing et al. used short-term HRV measurements as a marker of diabetic autonomic neuropathy.21 In 1977, Wolf et al.9 showed that patients with reduced HRV after a myocardial infarction had an increased mortality, and this was confirmed by studies showing that HRV is an accurate predictor of mortality post myocardial infarction (MI).22–24 HRV falls within 2 to 3 days after MI, begins to recover within a few weeks, and is maximally but not fully recovered by 6 to 12 months.25 Patients with persisting low HRV have mortality almost three times greater than those with a normal HRV.23
Over the last decade, alterations in HRV have been found in patients with many cardiovascular conditions. Patients with hypertension exhibit increased LFP and reduced circadian patterns,26 congestive heart failure is associated with reduced vagal but preserved sympathetic activity,27 and patients with denervated transplanted hearts show a 90% reduced HRV.28 HR and ULFP may be good prognostic indicators for mortality, progression to surgery and the development of atrial fibrillation in patients with mitral regurgitation,29 and patients with mitral valve prolapse show reduced HFP.30 Radio frequency ablation of supraventricular arrhythmia pathways leads to an increase in HR, reduced HRV and vagal tone measurements,31 and patients with cardiomyopathies exhibit reduced vagal tone.32 HRV has also been extensively investigated as a tool to predict the risk of sudden cardiac death. Low HRV is an independent risk factor for the development of later cardiac arrest in survivors of cardiac arrest.33 Both reduced HF power and reduced LF power are independent predictors of later sudden death following survival from cardiac arrest. Reduction in HF power appears superior at risk-stratifying patients.34 To date, most studies have concentrated on identifying HRV characteristics to predict the longer-term risk of developing fatal ventricular tachyarrhythmias (VTAs). Much less research has focussed on the changes that occur in HRV in the period immediately prior to the development of VTAs.
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Problems with measuring HRV |
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TopIntroductionThe history of HRVProblems with measuring HRVMeasuring HRVHRV and the onset...Conclusions and further workReferences |
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To detect HRV changes over a period of hours or days requires a large volume of ECG data to be collected and analysed. This has traditionally been done with Holter devices that record the ECG in out-patients over periods from 24 h up to several weeks. Data can also be collected from patients who are monitored in hospital (Figure 2). Data capture on dynamic changes in HRV in the period prior to arrhythmias or ischaemic events is harder to attain, due to the relative infrequency of such events. In the laboratory environment, studies of patients with exercise or electrically-induced VT are possible, and implanted cardio-defibrillator devices (ICDs), are able to store information prior to an episode of ventricular fibrillation (VF) or ventricular tachycardia (VT).
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Signal quality and elimination of background ‘noise’ is important when analysing HRV. Interpretation of HRV is extremely difficult in patients who are not in sinus rhythm (e.g. atrial fibrillation), or those with an extremely irregular HR (Figure 3) or multiple ectopic (VE) beats. Most HRV studies exclude patients who are not in sinus rhythm, but there is controversy over the issue of multiple VEs. Some authors advocate excluding signals that contain more than 10 ectopic beats per hour.35 Others accept patients where ectopic beats comprise up to 5%,19 8%,36 10%37,38 or even 15%39 of all R-R intervals.
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If a signal with ectopic beats is to be analysed, most authors advise removing the ectopic beats and correcting for them by adjusting the position of the R wave peak and placing a beat midway between the two adjacent beats. Editing algorithms are available that remove all R-R intervals that differ by a certain percentage from the preceding normal one. The level at which this editorial exclusion is performed (usually between 20–30% of the preceding R-R interval40) obviously reflects a balance between editing genuine data and missing ectopic beats. The frequency of ectopic beats is also of interest, since their frequency can increase prior to an arrhythmic event.41 Since the vast majority of patients at risk of sudden cardiac death have underlying cardiac abnormalities and are more likely to have irregular rhythms such as atrial fibrillation and multiple VEs, the analysis of these signals is problematic. The process of signal editing prior to analysis is complex, and is poorly performed by conventional algorithms. Some authors suggest that manual filtering, although time consuming, is more accurate.
A final problem when measuring HRV is of accurately locating the successive R-wave peaks on the ECG. This requires a robust R-wave detector algorithm. The more accurate the R-wave detector, the less error in the analysed HRV spectrum. A completely missed R wave will cause greater error than a slightly miscorrected R wave, and this error is reflected more in the HFP than in the LFP of the HRV spectrum. This is due to the greater influence of a single N-N interval on short-term variability measures (HFP), compared to longer-term variability measures (LFP) where a single N-N interval effect becomes smoothed out.
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Measuring HRV |
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TopIntroductionThe history of HRVProblems with measuring HRVMeasuring HRVHRV and the onset...Conclusions and further workReferences |
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HRV can be measured in time or frequency domains. Time domain methods are the simplest to perform. Each N (or R) point is determined in the ECG trace and variables such as mean HR and longest and shortest N-N intervals calculated. More complex calculations such as SDNN (standard deviation of the N-N intervals, representing the overall HRV) and NN50 (the number of adjacent N-N intervals that differ by more than 50 ms) can be performed using this data. Variables can also be derived that estimate the short- and long-term components of HRV (i.e. RMSDD, the square root of the mean squared differences between adjacent N-N intervals gives an estimate of short-term HRV, and SDANN, the standard deviation of the average N-N interval over periods of about 5 min, gives an estimate of long-term HRV). The calculation of all these variables enables the temporal variability of the HR to be quantified. The contribution of the various factors that manifest themselves in HF and LF HR changes can also be quantified (i.e. para-sympathetic and sympathetic influences) and the interactions between them, preceding an event such as a VTA, quantified.
Spectral methods have been used to analyse HRV for 40 years.42 These measure how the variance (or power) of the ECG signal changes as a function of frequency. Non-parametric methods of spectral analysis employing the Fast Fourier Transform (FFT) algorithm are commonly used.1 This technique involves splitting the ECG waveform into small subunits (usually from 2 to 5 min long for the measurement of HFP, LFP and VLFP, but can be up to 24 h when analysing ULF components). These signal segments are then ‘transformed’ from a temporal signal into a spectral representation whereby the ECG signal is reinterpreted as the sum of multiple simpler (sinusoidal) waves of a given amplitude and frequency (Figure 4). The amplitudes of the component waves are then plotted to give a power spectrum by plotting power (the square of amplitude in volts) versus frequency.
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VLFP, LFP and HFP components of the HRV can be calculated for recordings of 5 min or greater. For longer recordings of 24 h, ULFP can also be calculated, and reflects influences that occur on the heart rate over periods of days.
More recently, new time-frequency signal analysis methods have been used in the analysis of HRV. As their name suggests, these offer simultaneous interpretation of the signal in both time and frequency, which allows local, transient or intermittent components to be elucidated. (These are often obscured due to the averaging inherent within spectral-only methods, i.e. the FFT.) Several time-frequency methods are currently available, including the short time Fourier transform (STFT), Wigner-Ville transform (WVT), Choi-Williams distribution (CWD) and the continuous wavelet transform (CWT). Of these, the CWT has become the most favoured tool by researchers, as it does not contain the cross-terms inherent in the WVT and CWD methods, and provides frequency-dependent windowing, which allows for arbitrarily high resolution of the high frequency signal components (unlike the STFT)43 (Figure 5). Accordingly, high frequency components (the ‘fine detail’ of the ECG signal) are not lost to analysis. CWT has recently shown an increase in LFP:HFP ratio prior to the onset of non-sustained VTA.44 Other recent studies involving the wavelet analysis of HRV have allowed the detection of patterns directly associated with changes in myocardial perfusion,45 and the association between autonomic tone and spontaneous coronary spasm in patients with variant angina.46
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HRV and the onset of VTAs |
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TopIntroductionThe history of HRVProblems with measuring HRVMeasuring HRVHRV and the onset...Conclusions and further workReferences |
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VTAs have a circadian rhythm,47 with increased frequency during the early morning (7–11 am) and early evening (6–7 pm). This is consistent with Peckova's findings of a circadian distribution of cardiac arrests, with a low incidence at night, and peaks between 0800–1100 h and 1600–1900 h.48 The evening peak may be attributable to VF,48 while the morning peak may be attributable to patients in non-VF and non-VT rhythms. These findings have been replicated in other studies.49,50
Diurnal variation is also found in HRV. Higher LFP occurs in the daytime, and higher HFP at night.5,6 Markers of vagal activity (RMSSD and HFP) display circadian variability and are maximal during sleep. An inverse circadian rhythm is seen in patients with a morning VTA peak.51,52
The characteristics of HRV immediately prior to the onset of VTAs are, however, unclear. Some studies report significant changes in HRV in the period immediately preceding a VTA, and HR increasing prior to an episode of VTA is common.36,41,53–56 Vybrial et al., however, found no consistent changes in HRV indices (or HR) in 24 patients wearing Holter devices who developed VF.57 Some studies have reported an increase in HR, but also no change in HRV spectra characteristics prior to VTAs.53
Huikuri et al. found a significant reduction in HR, SDANN, HFP, LFP and VLFP in post-MI patients who developed VT or cardiac arrest compared to normal controls and post-MI patients not suffering arrhythmias.58 These changes occurred in the 1-h period prior to the onset of the VTA, and were more pronounced in patients developing sustained VT than those with non-sustained VT. These findings were confirmed by Shusterman et al., who noted a rise in HR and a fall in LFP, and LFP:HFP ratio before the onset of VT.59 Pruvot et al. found an increase in HR and a significant reduction in HRV prior to the onset of a VTA in post-MI patients.19 Other studies have shown a rise in VLF power and a decline in HF power,60 a decline in HF power but no VLF power changes,19 and a rise in LF:HF ratio.36,44
These results strongly suggest an alteration in the interaction between the sympathetic and parasympathetic nervous system prior to the onset of VTAs. The effect on HR and HRV variables is likely to be heterogeneous and affected by individual patient characteristics, which may explain the conflicting evidence in the literature. This is compounded by the small numbers of patients studied, their differing drug treatment, underlying cardiac pathology, pre-existing medical problems and methods of recording and analysing the ECG.
Two recent studies have addressed these difficulties. Meyerfeldt et al. found more reduced HRV periods before VT occurred in patients with ICDs.37 They divided the VT events into those with a cycle length <270 ms (‘fast VT’) and those with a cycle length >270 ms (‘slow VT’). Episodes of slow VT occurred during periods of increased HR, whereas fast VT occurred during periods of decreased HR. Shusterman et al. have suggested that baseline levels of HRV may determine HRV changes in response to short-term autonomic perturbations prior to VTAs.54 Groups with higher initial total power, LFP and LFP: HFP ratio exhibited a fall in LFP in the 2 h prior to the VT episode. This group had a fall in HFP in the 15 min prior to VT onset. A group with lower initial total power, LFP and LFP:HFP HRV parameters, showed an increase or no change in LFP in the 2 h prior to VT, and no change in HFP immediately preceding VT. This suggests that merely a change in HRV, rather than the magnitude or nature of the change, facilitates the development of VTAs.
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Conclusions and further work |
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TopIntroductionThe history of HRVProblems with measuring HRVMeasuring HRVHRV and the onset...Conclusions and further workReferences |
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Initially, the focus of HRV investigation was its use in the prediction of long-term survival in patients who had suffered myocardial infarction, or had valvular or congestive heart disease. More recently, work has concentrated on attempts to predict the timing of onset of fatal VTAs. Given the poor results currently achieved in out-of-hospital cardiac arrest, efforts directed towards the identification of VTAs are crucial.61 The area of HRV behaviour before the onset of life-threatening VTAs offers exciting possibilities. Newer improved analytical techniques such as wavelet analysis, together with improved processing power, have simplified and speeded up signal acquisition and analysis.
While the prognostic value of HRV post-myocardial infarction is well established, evidence of value in VTAs and sudden death is less clear. Although it is not yet possible to predict the onset of ventricular arrhythmias using HRV techniques, there is now a better understanding of HRV behaviour before these events. Prediction of the exact time of onset of VTA occurrence remains a distant and probably unrealistic goal using HRV analysis alone. It is, however, likely that patients with a high risk of developing VTAs may be identified with some accuracy, enabling prophylactic approaches to be made. Results of trials such as AVID,62 MUSTT,63 and MADIT I and II64 have shown that ICDs improve survival in patients surviving an episode of cardiac arrest, and in those with coronary heart disease and advanced left ventricular dysfunction. HRV investigation, together with other ECG markers (e.g. T wave alternans), may further risk-stratify these patients,65,66 enabling the targeting of groups who may benefit from such devices.
Recent awareness of the heterogeneity of HRV behaviour has emphasized the importance of acquiring large ECG databases from patients at risk of developing life-threatening VTAs.67 The increasing ease with which data can be captured from monitor-defibibrillator devices used in and out of hospital should facilitate this process and enable future algorithm development.
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Footnotes |
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Address correspondence to Dr M.J. Reed, Emergency Department, Royal Infirmary of Edinburgh, 51 Little France Crescent, Edinburgh EH16 4SA. e-mail: mattreed1{at}hotmail.com
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References |
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TopIntroductionThe history of HRVProblems with measuring HRVMeasuring HRVHRV and the onset...Conclusions and further workReferences |
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1. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task for of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J 1996; 17:354–81.
2. Akselrod S, Gordon D, Ubel FA, et al. Power spectrum analysis of heart rate fluctuation: a quantitive probe of beat-to-beat cardiovascular control. Science 1981; 213:220–2.
3. Pomeranz B, Macauley RJ, Caudill MA, et al. Assessment of autonomic function in humans by heart rate spectral analysis. Am J Physiol 1985; 248:H151–3.
4. Task force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 1996; 93:1043–65.
5. Malliani A, Pagani M, Lombardi F, Cerruti S. Cardiovascular neural regulation explored in the frequency domain. Circulation 1991; 84:1482–92.
6. Furlan R, Guzetti S, Crivellaro W, et al. Continuous 24-hour assessment of the neural regulation of systemic arterial pressure and RR variabilities in ambulant subjects. Circulation 1990; 81:537–47.
7. Kamath MV, Fallen EL. Power spectral analysis of heart rate variability: a non-invasive signature of cardiac autonomic function. Crit Revs Biomed Eng 1993; 21:245–311.
8. Buchanan M. Fascinating rhythm. New Scientist 1998; 3 January:20–25.
9. Wolf MM, Varigos GA, Hunt D, et al. Sinus arrhythmia in acute myocardial infarction. Med J Australia 1978; 2:52–3.
10. Armour JA, Hageman GR, Randall WC. Arrhythmias induced by local cardiac nerve stimulation. Am J Physiol 1972; 223:1068–75.
11. Han J, Garcia de Jalon P, Moe GK. Adrenergic effects on ventricular vulnerability. Circ Res 1964; 14:516–24.
12. Hockman CH, Mauck HP Jr, Hoff EC. ECG changes resulting from cerebral stimulation. II. Spectrum of ventricular arrhythmias of sympathetic origin. Am Heart J 1966; 71:695–700.[CrossRef][ISI][Medline]
13. Maling HM, Moran NC. Ventricular arrhythmias induced by sympathomimetic amines in unanaesthetized dogs following coronary occlusion. Circ Res 1957; 5:409–13.
14. Kliks BR, Burgess MJ, Abildskov JA. Influence of sympathetic tone on ventricular fibrillation threshold during experimental coronary artery occlusion. Am J Cardiol 1975; 36:45–9.[CrossRef][ISI][Medline]
15. Kent KM, Smith ER, Redwood DR, et al. Electrical stability of acutely ischemic myocardium. Influences of heart rate and vagal stimulation. Circulation 1973; 47:291–8.
16. Myers RW, Pearlman AS, Hyman RM, et al. Beneficial effects of vagal stimulation and bradycardia during experimental acute myocardial infraction. Circulation 1974; 49:943.
17. Lin JL, Chan HL, Du CC, et al. Long-term beta-blocker therapy improves autonomic nervous regulation in advanced congestive heart failure: a longitudinal heart rate variability study. Am J Cardiol 1999; 137:658–65.
18. Pousset F, Copie X, Lechat P, et al. Effects of bisoprolol on heart rate variability in heart failure. Am J Cardiol 1996; 77:612–17.[CrossRef][ISI][Medline]
19. Pruvot E, Thonet G, Vesin JM, et al. Heart rate dynamics at the onset of ventricular tachyarrhythmias as retrieved from implantable cardioverter-defibrillators in patients with coronary artery disease. Circulation 2000; 101:2398–404.
20. Hon EH, Lee ST. Electronic evaluations of the fetal heart rate patterns preceding fetal death, further observations. Am J Obstet Gynae 1965; 87:814–26.
21. Ewing DJ, Martyn CN, Young RJ, Clarke BF. The value of cardiovascular autonomic function tests: 10 years experience in diabetes. Diabetes Care 1985; 8:491–8.[Abstract]
22. Bigger JT Jr, Fleiss JL, Steinman RC, Rolnitzky LM. Kleiger RE, Rottman JN. Frequency domain measures of heart period variability and mortality after myocardial infarction. Circulation 1993; 85:164–71.
23. Kleiger RE, Miller JP, Bigger JT Jr, Moss AJ. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol 1987; 59:256–62.[CrossRef][ISI][Medline]
24. Malik M, Farrell T, Cripps T, Camm AJ. Heart rate variability in relation to prognosis after myocardial infarction: selection of optimal processing techniques. Eur Heart J 1989; 10: 1060–74.
25. Bigger JT, Fleiss JL, Rolnitzky LM, et al. Time course of recovery of heart period variability after myocardial infarction. J Am Coll Cardiol 1987; 59:256–62.
26. Guzzetti S, Dassi S, Pecis M, et al. Altered pattern of circadian neural control of heart period in mild hypertension. J Hypertension 1991; 9:831–8.[CrossRef][ISI][Medline]
27. Casolo G, Balli E, Taddei T, et al. Decreased spontaneous heart rate variability on congestive cardiac failure. Am J Cardiol 1989; 64:1162–7.[CrossRef][ISI][Medline]
28. Sands KE, Appel ML, Lelly LS, et al. Power spectrum analysis of heart rate variability in human cardiac transplant recipients. Circulation 1989; 79:76–82.
29. Stein KM, Bores JS, Hoxchreites C, et al. Prognostic value and physiological correlates of heart rate variability in chronic severe mitral regurgitation. Circulation 1993; 88:127–35.
30. Marangoni S, Scalvini S, Mai R, et al. Heart rate variability assessment in patients with mitral valve prolapse syndrome. Am J Noninvas Cardiol 1993; 7:210–14.
31. Kocovic DZ, Harada T, Shea JB, et al. Alterations of heart rate and of heart rate variability after radiofrequency catheter ablation of supraventricular tachycardia. Circulation 1993; 88:1671–81.
32. Counihan PJ, Fei L, Bashir Y, et al. Assessment of heart rate variability in hypertrophic cardiomyopathy. Association with clinical and prognostic features. Circulation 1993; 88:1682–90.
33. Dougherty CM, Burr RL. Comparison of heart rate variability in survivors and nonsurvivors of sudden cardiac arrest. Am J Cardiol 1992; 70:441–8.[CrossRef][ISI][Medline]
34. Algra A, Tijssen JG, Roelandt JR, et al. Heart rate variability from 24-hour electrocardiography and the 2-year risk for sudden death. Circulation 1993; 88:180–5.
35. Kleiger RE, Stein PK, Bosner MS, et al. Time-domain measurements of heart rate variability. In: Malik M, Camm AJ, eds. Heart rate variability. Armonk NY, Futura, 1995; 33–46.
36. Lombardi F, Porta A, Marzegalli M, et al. Implantable Cardioverter Defibrillator-Heart Rate Variability Italian Study Group. Heart rate variability patterns before ventricular tachycardia onset in patients with an implantable cardioverter defibrillator. Participating Investigators of ICD-HRV Italian Study Group. Am J Cardiol 2000; 86:959–63.[CrossRef][ISI][Medline]
37. Meyerfeldt U, Wessel N, Schutt H, et al. Heart rate variability before the onset of ventricular tachycardia: differences between slow and fast arrhythmias. Int J Cardiol 2002; 84:141–51.[CrossRef][ISI][Medline]
38. Tsuji H, Venditti FJ, Manders ES, et al. Determinants of heart rate variability. JACC 1996: 28:1539–46.[Abstract]
39. Ozdemir O, Soylu M, Demir AD, et al. Sympathetic overactivity as cause of exercise-induced ventricular tachycardia in patients with normal coronary arteries. Texas Heart Inst J 2003; 30:100–4.[ISI][Medline]
40. Valkama JO, Huikuri HV, Koistinen J, et al. Relation between heart rate variability and spontaneous and induced ventricular arrhythmias in patients with coronary artery disease. JACC 1995; 25:437–43.[Abstract]
41. Nemec J, Hammill SC, Shen WK, et al. Increase in heart rate precedes episodes of ventricular tachycardia and ventricular fibrillation in patients with implantable cardioverter defibrillators. Pacing Clin Electrophysiol 1999; 22:1729–38.[CrossRef][Medline]
42. Kay SM, Marple SL. Spectrum analysis: a modern perspective. Proc IEEE 1981; 69:1380–419.
43. Addison PS. The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. Bristol, Institute of Physics Publishing, 1992.
44. Chen S-W. A wavelet-based heart-rate variability analysis for the study of nonsustained ventricular tachycardia. IEEE Transact Biomed Eng 2002; 49:736–42.[CrossRef]
45. Toledo E, Gurevitz O, Hod H, et al. Wavelet analysis of instantaneous heart rate: a study of autonomic control during thrombosis. Am J Physiol Regul Integr Comp Physiol 2003; 284:R1079–91.
46. Tan B-H, Shimizu H, Hiromoto K, et al. Wavelet transform analysis of heart rate variability to assess the autonomic changes associated with spontaneous coronary spasm of variant angina. J Electrocardiol 2003; 36:117–24.[CrossRef][ISI][Medline]
47. Kozak M, Krivan L, Semrad B. Circadian variations in the occurrence of ventricular tachyarrhythmias in patients with implantable cardioverter defibrillators. Pacing Clin Electrophysiol 2003; 26:731–5.[CrossRef][Medline]
48. Peckova M, Fahrenbruch CE, Cobb LA, et al. Circadian variations in the occurrence of cardiac arrests: initial and repeat episodes. Circulation 1998; 98:31–9.
49. Levine RL, Pepe PE, Fromm RE, et al. Prospective evidence of a circadian rhythm for out-of-hospital cardiac arrests. JAMA 1992; 267:2935–7.[Abstract]
50. Soo LH, Gray D, Young T, Hampton JR. Circadian variation in witnessed out of hospital cardiac arrest. Heart 2000; 84:370–6.
51. Fries R, Hein S, Konig J. Reversed circadian rhythms of heart rate variability and morning peak occurrence of sustained ventricular tachyarrhythmias in patients with implanted cardioverter defibrillator. Med Sci Mon 2002; 8:CR751–6.
52. Sezgin A, Demircan S, Yalcin F. Reversed circadian rhythms of heart rate variability and morning peak occurrence of sustained ventricular tachyarrhythmias in patients with implanted cardioverter defibrillator. Med Sci Mon 2003; 9:LE1, author reply LE2.
53. Huikuri HV, Seppanen T, Koistinen MJ, Airaksinen J, Ikaheimo MJ, Castellanos A, Myerburg RJ. Abnormalities in beat-to-beat dynamics of heart rate before the spontaneous onset of life-threatening ventricular tachyarrhythmias in patients with prior myocardial infarction. Circulation 1996; 93:1836–44.
54. Shusterman V, Aysin B, Weiss R, et al. Dynamics of low-frequency R-R interval oscillations preceding spontaneous ventricular tachycardia. Am Heart J 2000; 139:126–33.[ISI][Medline]
55. Pruvot E, Thonet G, Vesin JM, et al. Heart rate dynamics at the onset of ventricular tachyarrhythmias as retrieved from implantable cardioverter-defibrillators in patients with coronary artery disease. Circulation 2000; 101:2398–404.
56. Lombardi F, Porta A, Marzegalli M, et al. Heart rate variability patients before ventricular tachycardia onset in patients with an implantable cardioverter defibrillator. Am J Cardiol 2000; 86:959–63.[CrossRef][ISI][Medline]
57. Vybiral T, Glaeser DH, Goldberger AL, et al. Conventional heart rate variability analysis of ambulatory electrocardiographic recordings fails to predict imminent ventricular fibrillation. J Am Coll Cardiol 1993; 22:557–65.[Abstract]
58. Huikuri HV, Valkama JO, Airaksinen KE, et al. Frequency domain measures of heart rate variability before the onset of nonsustained and sustained ventricular tachycardia in patients with coronary artery disease. Circulation 1993; 87:1220–8.
59. Shusterman V, Aysin B, Gottipaty V, et al. Autonomic nervous system activity and the spontaneous initiation of ventricular tachycardia. ESVEM Investigators. Electrophysiologic Study Versus Electrocardiographic Monitoring Trial. J Am Coll Cardiol 1998; 32:1891–9.
60. Thonet G, Pruvot E, Vesin JM, et al., for the HRVF Study Investigators. New insights into ventricular tachyarrhythmia onset using heart rate signal decomposition. Proc 19th Ann Int Conf IEEE Eng Med Biol Soc 1997; 1440–1.
61. Holmberg M, Holmberg S, Herlitz J, et al. Survival after cardiac arrest outside hospital in Sweden. Resuscitation 1998; 36:29–36.[CrossRef][ISI][Medline]
62. The Antiarrhythmics versus Implantable Defibrillators (AVID) Investigators. A comparison of antiarrhythmic-drug therapy with implantable defibrillators in patients resuscitated from near-fatal ventricular arrhythmias. N Engl J Med 1997; 337:1576–83.
63. Prystowsky EN. Nisam S. Prophylactic implantable cardioverter defibrillator trials: MUSTT, MADIT, and beyond. Multicenter Unsustained Tachycardia Trial. Multicenter Automatic Defibrillator Implantation Trial. Am J Cardiol 200; 86:1214–15.
64. Moss AJ. MADIT-I and MADIT-II (review). J Cardiovasc Electrophysiol 2003; 14:S96–8.[ISI][Medline]
65. Zareba W, Moss AJ. Non-invasive risk stratification in post-infarction patients with severe ventricular dysfunction and methodology of the MADIT II non-invasive electrocardiology sub study (review). J Electrophysiol 2003; 36:S101–8.
66. Cohen RJ. Enhancing specificity without sacrificing sensitivity: potential benefits of using microvolt T-wave alternans testing to risk stratify the MADIT-II population. Cardiac Electrophysiol Rev 2003; 7:438–42.[CrossRef][Medline]
67. Romero Legarreta I, Addison PS, Reed MJ, et al. Continuous Wavelet Modulus Maxima and the Analysis of the Electrocardiogram: Beat Characterisation and Interbeat Measurement. Int J Wavelets Multiresolution Inf Processing 2005; in press.
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  R. Ranpuria, M. Hall, C. T. Chan, and M. Unruh Heart rate variability (HRV) in kidney failure: measurement and consequences of reduced HRV Nephrol. Dial. Transplant., February 1, 2008; 23(2): 444 - 449. [Abstract] [Full Text] [PDF]
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