SciELO - Scientific Electronic Library Online

 
vol.104 issue6Depression as a Clinical Determinant of Dependence and Low Quality of Life in Elderly Patients with Cardiovascular DiseasePost-Acute Coronary Syndrome Alcohol Abuse: Prospective Evaluation in the ERICO Study author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

Share


Arquivos Brasileiros de Cardiologia

Print version ISSN 0066-782XOn-line version ISSN 1678-4170

Arq. Bras. Cardiol. vol.104 no.6 São Paulo June 2015  Epub Apr 14, 2015

http://dx.doi.org/10.5935/abc.20150032 

Original Articles

Assessment of Autonomic Function by Phase Rectification of RRInterval Histogram Analysis in Chagas Disease

Olivassé Nasari Junior1 

Paulo Roberto Benchimol-Barbosa1  3 

Roberto Coury Pedrosa2 

Jurandir Nadal1 

1Programa de Engenharia Biomédica – COPPE/UFRJ, Rio de Janeiro, RJ – Brazil

2Hospital Universitário Clementino Fraga Filho – UFRJ, Rio de Janeiro, RJ – Brazil

3Hospital Universitário Pedro Ernesto – UERJ, Rio de Janeiro, RJ – Brazil

ABSTRACT

Background:

In chronic Chagas disease (ChD), impairment of cardiac autonomic function bears prognostic implications. Phase‑rectification of RR-interval series isolates the sympathetic, acceleration phase (AC) and parasympathetic, deceleration phase (DC) influences on cardiac autonomic modulation.

Objective:

This study investigated heart rate variability (HRV) as a function of RR-interval to assess autonomic function in healthy and ChD subjects.

Methods:

Control (n = 20) and ChD (n = 20) groups were studied. All underwent 60-min head-up tilt table test under ECG recording. Histogram of RR-interval series was calculated, with 100 ms class, ranging from 600–1100 ms. In each class, mean RR-intervals (MNN) and root-mean-squared difference (RMSNN) of consecutive normal RR-intervals that suited a particular class were calculated. Average of all RMSNN values in each class was analyzed as function of MNN, in the whole series (RMSNNT), and in AC (RMSNNAC) and DC (RMSNNDC) phases. Slopes of linear regression lines were compared between groups using Student t-test. Correlation coefficients were tested before comparisons. RMSNN was log-transformed. (α < 0.05).

Results:

Correlation coefficient was significant in all regressions (p < 0.05). In the control group, RMSNNT, RMSNNAC, and RMSNNDCsignificantly increased linearly with MNN (p < 0.05). In ChD, only RMSNNAC showed significant increase as a function of MNN, whereas RMSNNT and RMSNNDC did not.

Conclusion:

HRV increases in proportion with the RR-interval in healthy subjects. This behavior is lost in ChD, particularly in the DC phase, indicating cardiac vagal incompetence.

Key words: Chagas Disease; Chagas Cardiomyopathy; Heart Rate; Organ Dysfunction Scores; Sympathetic Nervous System; Charts; Statistics as Topic

Introduction

Chagas disease (ChD) is a major cause of cardiomyopathy in Latin America. It has been estimated that 8–11 million people are currently infected by Trypanosoma cruzi worldwide, potentially becoming a significant healthcare-related problem in Europe and in the United States due to migration 1,2. In chronic ChD, autonomic dysfunction has been associated with impairment of both parasympathetic and sympathetic limbs3-5, with prognostic implications6.

Heart rate variability (HRV) analysis is a powerful and simple method for assessing autonomic influence on the sinus node and risk stratification in many cardiac diseases7,8. On routine clinical assessment, parameters in time domain are usually estimated during a predefined time sequence of normal-to-normal RR-intervals . Among the parameters usually employed, root-mean-squared difference (RMSNN) is particularly useful, as it expresses the amount of energy associated with data variability7. However, none of these indexes distinguish between vagal and sympathetic effects.

Recently, an approximate isolation of distinct autonomic contribution on heart rate (HR) has been possible by assessing the capability of RR-interval series to accelerate (AC) or decelerate (DC), representing sympathetic and parasympathetic contributions, respectively. To further accomplish this task, it was initially detected that if a particular RR-interval changed relative to the previous one, the corresponding RR- interval was separated in a new series 9,10.

In healthy subjects, it has been demonstrated that HRV indices tended to increase as RR- intervals enlarged 11,12. On the other hand, this relationship may be lost during a disease state and may further precipitate some forms of ventricular arrhythmia, such as long QT syndrome, and ischemic cardiomyopathy13. Thus, the aims of this study were (i) to assess RMSNN index on AC and DC phases of RR-interval series in order to isolate sympathetic and parasympathetic effects, respectively, and (ii) to correlate RMSNN and mean RR- intervals (MNN) to assess heart rate dependence of autonomic modulation index in chronic ChD.

Methods

Study population

ECG signals were extracted from an existing high resolution ECG database 14. The study protocol was approved by the Hospital Universitário Clementino Fraga Filho Ethics Committee and informed consent was obtained from each volunteer. A group of gender-matched 20 healthy sedentary participants [Control group, (mean age ± SD) 51.1 ± 17.6 years] and 20 subjects with chronic ChD (Chagas group, 55 ± 10.3 years) were studied. Chronic ChD subjects were enrolled to the study based on spontaneous demand. Due to the exploratory nature of the study, the number of participants was arbitrarily defined and equally distributed between groups.

According to surface ECG data analysis, in ChD group, seven had normal ECG. Among 13 subjects with abnormal ECG, nine showed left atrial overload based on Morris criteria, nine had left anterior fascicular block, nine had complete right bundle branch block and one had first degree AV block. Two subjects showed isolated supraventricular tachycardia and four showed isolated ventricular premature beats.

Additionally, all participants met the following criteria: (i) no intake of nutritional supplements or potential ergogenic aids of any type (e.g., exogenous anabolic androgenic steroids); (ii) non-smokers; (iii) normal blood pressure; (iv) non-diabetic; (v) no history of alcohol addiction; (vi) no history of thyroid dysfunction; and (vii) not taking medications that affect cardiac electrical properties and/or autonomic function.

Signal acquisition and processing

All subjects underwent 60-min head-up tilt test (HUTT) under modified Westminster protocol 15 at 70° and continuous high-resolution ECG recording in an acclimatized (27°C) and quiet room. Subjects were oriented to withhold exercise for 48 h before the exam, fast for at least 4 h, and avoid taking caffeine-containing beverages on the day of the exam. Before ECG recording, subjects remained in the supine position for at least 5 min in order to reduce orthostatic autonomic memory on spontaneous RR- interval variations16,17.

ECG signal acquisition periods were characterized by 10 min of supine rest followed by 40-min HUTT and another 10-min supine rest. Accordingly, HRV was expected to be influenced by two predominant autonomic inputs: parasympathetic input during supine rest, and sympathetic input during tilt 7.

High-resolution ECG signals were acquired using modified bipolar Frank XYZ orthogonal leads 18. Digital data were processed with custom-made pattern recognition software19-21. The analysis of the HRV was done by extraction of the normal RR- intervals, after detection of the QRS complex using a low-pass triangular filter. Any RR- interval that exhibited more than 20% change from the previous RR-interval were excluded, as they were likely to be related to measurement noise or ectopic beats21,22.

Instantaneous RR-interval analysis

The RR- interval histogram was constructed for each individual series and split into 100-ms width classes, ranging from 600–1100 ms. For each histogram class, and respective to each RR-interval series, mean (MNN) and root-mean-square diference (RMSNN) of consecutive normal RR-intervals suiting a particular class were calculated. Only the pairs of consecutive normal RR-intervals for individual series that were inside a particular class of the RR histogram were analyzed together.

For a particular histogram class (class) of the i th series, containing Ni,class RR -intervals, calculation of the mean (Mi,class) and the root-mean-squared difference (RMSi,class) of the normal RR -intervals was performed as follows:

For each histogram, classes with intervals of 30 or less were excluded to avoid bias due to lack of statistical precision.

The values of the variables Mi,class and RMSi,class were aggregated to the respective histogram class. The ensemble mean (MNN class) and root-mean-squared difference (RMSNNclass) of RR-intervals for each histogram class, weighed by the respective degree‑of‑freedom (ηi,class), were calculated according to:

Instantaneous AC and DC analysis

RR- interval histograms in AC and DC phases were also built following the procedures described above; RMSNN in AC (RMSNNAC) and RMSNNin DC (RMSNNDC) phases were calculated accordingly. To further accomplish this task, data points were initially isolated as acceleration (AC) or deceleration (DC) capacities. If a particular RR-interval increased relative to the previous one, a DC interval occurred. As the instantaneous RR‑interval increased, it represented a parasympathetic action (DC; lozenge symbols in Figure 1). Conversely, a sympathetic effect on the RR-interval was represented whenever the RR‑interval decreased relative to the previous one, and AC interval was defined (AC; represented by circle symbols in Figure 1).

Figure 1 Deceleration and Acceleration anchor points are represented in RR-intervals samples that were derived from an ECG recording. RR-interval histogram is represented on the right 

Statistical analysis

The RMSNN and MNN of each subject were pooled and averaged on a class-by-class basis in the control and ChD groups. RMSNN was analyzed in the whole series (RMSNNT) as well as in the AC and DC phases. Regression lines were analyzed and angular coefficient was compared between ChD and control groups using non-paired Student’s t-test. Correlation coefficients (r) were tested before each test. Due to strong asymmetry in their probability density functions, the RMSNNvariables were log-transformed before analysis to fit appropriately in the parametric statistical analysis. All tests were considered significant at α level < 0.05.

Results

Table 1 shows the linear correlation coefficient (r) and respective angular coefficient (slope) of the regression line between MNN and other each pooled variable. The r-values were significant in all regression lines (p < 0.05).

Table 1 Correlation of parameters: MNN vs. Variables 

Group   RMSNNT RMSNNAC RMSNNDC
Control r 0.96 * 0.99 * 0.99 *
slope 0.0011 * 0.0012 * 0.0008 *
ChD r -0.55 * 0.96 * -0.75 *
slope -0.0002 0.0010 * -0.0003

*p < 0.05.

The log-transformed pooled RMSNN (T, AC, and DC), as a function of pooled MNN, were presented for each group (Figure 2). RMSNNAC were significantly different in both groups, whereas, in the control group, RMSNNT, RMSNNAC, and RMSNNDC significantly increased proportionally to MNN (p< 0.05); in the ChD group, only RMSNN AC showed significant increase as a function of MNN, whereas RMSNNTandRMSNNDC did not.

Figure 2 Comparison of control and ChD groups in terms of logtransformed of pooled RMSNNT (a), RMSNNAC (b), and RMSNNDC (c) as a function of pooled RR-intervals (MNN), and the respective angular coefficient (slope) of regression line. p value refers to Student’s t-test significance for comparing slopes. Correlation was significant for all regression lines. (See text for details) 

Based on the total number of RR- intervals suiting a particular histogram class, the percent value (mean ± SD) of RR-interval pairs rejected as not pertaining to the same histogram class was 31.7% ± 21.7% for the control group and 27.0% ± 14.7% for ChD. Figure 3 shows histograms of RR- interval pairs for each group according to the AC and DC phases, respectively.

Figure 3 RR-interval pairs histograms assessed for each group and phase: acceleration phase (a) and deceleration phase (b). 

Discussion

Cardiac autonomic dysfunction, characterized mainly by parasympathetic depression, is an important aspect of human ChD3-5. The observation of marked autonomic dysfunction in association with normality of most ventricular echocardiographic variables suggested that there was no clear relationship between autonomic and ventricular function23. Additionally, autonomic dysfunction seemed to be a primary phenomenon, preceding ventricular mechanical changes in chronic ChD evolution6,24,25.

In a previous study22, DC index adaptation was proposed to measure cardiac vagal modulation by a phase-rectified signal averaging (PRSA) procedure10,11 that was effective in distinguishing athletes from sedentary healthy volunteers. It was hypothesized that depending on vagal stimulus intensity, the rate of ascent of the RR-interval series would change accordingly, determining slope variation. Thus, the strongest vagal stimulus determined the steepest slope and vice-versa, potentially affecting the DC value. Although PRSA has been originally developed to risk-stratify subjects post myocardial infarction10, its application in assessing physiological conditions that are strongly related to vagal activity modulation has been shown to be highly pertinent and feasible as well.

In the present study, the behavior of RR- intervals was analyzed by grouping time domain RMSNN parameters calculated at different histogram classes. This procedure made it possible to cluster beats under the influence of similar instantaneous time factors. Additionally, assessment of the capacity of RR-interval series to accelerate or decelerate enabled the isolation of both sympathetic (AC phase) and parasympathetic (DC phase) contributions on the RR- intervals series.

The essential point of this study was to stratify HR and HRV according to instantaneous RR-interval difference using a parameter that expresses energy (RMSNN) from all series and to isolate sympathetic and parasympathetic contributions. Also, it introduced novel information that represents insights into the dependence of autonomic modulation on heart rate in a population of chronic ChD.

In the control group, HRV (RMSNNT, RMSNNAC and RMSNNDC) was strongly dependent on the instantaneous RR- interval, confirming the previous findings of Benchimol-Barbosa et al.13. In the physiological range of RR- interval variation (600–1100 ms), RMSNN was lower during head-up tilt and higher during supine position, representing sympathetic and parasympathetic autonomic influences on HRV, respectively. Moreover, it was notable that RR-interval variation had average inter-beat “jumps” that were proportional to the average RR-intervals (Figure 2). This relation was also represented by a strong linear dependence between RMSNN and MNN(r > 0.96).

On the other hand, in the ChD group, only RMSNNAC showed significant increase as a function of MNN(p < 0.05), which assessed the isolated contribution of sympathetic nervous system on HRV. RMSNNDC, which assessed the isolated parasympathetic influence, not only had its mean value lower than the control group, but also showed no variations with changes in mean RR- interval. These findings indicate that not only vagal modulation was reduced in this population, but also the ability of parasympathetic system to modulate RR-intervals at different heart rates throughout a wide range of RR-intervals analyzed. We named this later observation as parasympathetic incompetence.

This study has its limitations, including a relatively small sample size and application of the method using two physiologically well-defined groups. Although the groups were not matched by age, both had their mean age above 40 years. Assessment of left ventricular systolic function was not carried out in the present study; however, in Chagas disease, there was no clear relationship between autonomic and ventricular function24. Further studies are needed to confirm present findings.

Conclusion

In subjects with chronic Chagas disease, a significant reduction of autonomic modulation of the heart is observed throughout a wide physiological range of RR-intervals.

Additionally, in healthy sedentary subjects, RMSNN increases proportionally with RR-interval. This relationship is not observed in chronic Chagas disease, particularly during parasympathetic stimulation phase, indicating parasympathetic incompetence in modulating heart rate variation in this scenario.

Acknowledgements

This work was partially supported by Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) and the Brazilian Agencies CNPq and CAPES. We wish to thank Dr. Aline Medeiros for her dedication in the acquisition of ECG data of the patients.

Sources of Funding

This study was partially funded by FAPERJ, CNPq e CAPES.

Study Association

This study is not associated with any thesis or dissertation work.

References

Dias JC, Silveira AC, Schofield CJ. The impact of Chagas' disease control in Latin America: a review. Mem Inst Oswaldo Cruz. 2002;97(5):603-12. [ Links ]

Guerri-Guttenberg RA, Grana DR, Ambrosio G, Milei J. Chagas cardiomyopathy: Europe is not spared! Eur Heart J. 2008;29(21):2587-91. [ Links ]

Gallo Junior L, Morelo Filho J, Maciel BC, Marin Neto JA, Martins LE, Lima Filho EC. Functional evaluation of sympathetic and parasympathetic system in Chagas' disease using dynamic exercise. Cardiovasc Res. 1987;21(12):922-7. [ Links ]

Junqueira Junior LF, Beraldo PS, Chapadeiro E, Jesus PC. Cardiac autonomic dysfunction and neuroganglionitis in a rat model of chronic Chagas' disease. Cardiovasc Res. 1992;26(4):314-9. [ Links ]

Dávila DF, Inglessis G, Mazzei de Dávila CA. Chagas' heart disease and the autonomic nervous system. Int J Cardiol. 1998;66(2):123-7. [ Links ]

Benchimol-Barbosa PR, Tura BR, Barbosa EC, Kantharia BK. Utility of a novel risk score for prediction of ventricular tachycardia and cardiac death in chronic Chagas disease - the SEARCH-RIO study. Braz J Med Biol Res. 2013;46(11):974-84. [ Links ]

Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J. 1996;17(3):354-81. [ Links ]

Rajendra Acharya U, Paul Joseph K, Kannathal N, Lim CM, Suri JS. Heart rate variability: a review. Med Biol Eng Comput. 2006;44(12):1031-51. [ Links ]

Bauer A, Kantelhardt JW, Barthel P, Schneider R, Mäkikallio T, Ulm K, et al. Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study. Lancet. 2006;367(9523):1674-81. [ Links ]

Bauer A, Kantelhardt JW, Bunde A, Bundec A, Barthela P, Schneider R, et al. Phase-rectified signal averaging detects quasi-periodicities in nonstationary data. Physica A. 2006;364:423-34. [ Links ]

Merri M. QT variability. In: Moss AJ, Stern S (eds). Noninvasive electrocardiology: clinical aspects of Holter monitoring. London: Saunders WB; 1996. p. 421-43. [ Links ]

Benchimol-Barbosa PR, Barbosa-Filho J, Cordovil I, Nadal J. The effect of the instantaneous RR interval on the dynamic properties of the heart rate and the ventricular repolarization duration variability. Computers in Cardiology. 2000;27:821-4. [ Links ]

Pitzalis MV, Mastropasqua F, Massari F, Totaro P, Di Maggio M, Rizzon P. Holter-guided identification of premature ventricular contractions susceptible to suppression by beta-blockers. Am Heart J. 1996;131(3):508-15. [ Links ]

Medeiros AS. Avaliação autonômica cardíaca em indivíduos com doença de Chagas sem disfunção ventricular empregando o teste de inclinação (Tilt-Test). [Tese]. Rio de Janeiro: Universidade Federal do Rio de Janeiro; 2008. [ Links ]

Bomfim AS, Benchimol-Barbosa PR, Barbosa EC, Boghossian SH, Ribeiro RL, Ginefra P. Teste de inclinação: fundamentos e aplicação clínica. Rev SOCERJ. 2004;17(4):243-50. [ Links ]

Lipsitz LA, Mietus J, Moody GB, Goldberger AL. Spectral characteristics of heart rate variability before and during postural tilt: relations to aging and risk of syncope. Circulation. 1990;81(6):1803-10. [ Links ]

Ryan SM, Goldberger AL, Ruthazer R, Mietus J, Lipsitz LA. Spectral analysis of heart rate dynamics in elderly persons with postprandial hypotension. Am J Cardiol. 1992;69(3):201-5. [ Links ]

Barbosa PR, Barbosa-Filho J, de Sá CA, Barbosa EC, Nadal J. Reduction of electromyographic noise in the signal-averaged electrocardiogram by spectral decomposition. IEEE Trans Biomed Eng. 2003;50(1):114-7. [ Links ]

Nasario-Junior O, Benchimol-Barbosa PR, Nadal J. Principal component analysis in high resolution electrocardiogram for risk stratification of sustained monomorphic ventricular tachycardia. Biomedical Signal Process Control. 2014;10:275-80. [ Links ]

Nasario-Junior O, Benchimol-Barbosa PR, Trevizani GA, Marocolo M, Nadal J. Effect of aerobic conditioning on ventricular activation: a principal components analysis approach to high-resolution electrocardiogram. Comput Biol Med. 2013;43(11):1920-6. [ Links ]

Nasario-Junior O, Benchimol-Barbosa PR, Nadal J. Refining the deceleration capacity index in phase-rectified signal averaging to assess physical conditioning level. J Electrocardiol. 2014; 47(3):306-10. [ Links ]

Clifford GD, McSharry PE, Tarassenko L. Characterizing artifact in the normal human 24-hour RR time series to aid identification and artificial replication of circadian variations in human beat to beat heart rate using a simple threshold. Computers in Cardiology. 2002;29:129-32. [ Links ]

Ribeiro AL, Moraes RS, Ribeiro JP, Ferlin EL, Torres RM, Oliveira E, et al. Parasympathetic dysautonomia precedes left ventricular systolic dysfunction in Chagas disease. Am Heart J. 2001;141(2):260-5. [ Links ]

Vasconcelos DF, Junqueira LF Jr. Cardiac autonomic and ventricular mechanical functions in asymptomatic chronic Chagasic cardiomyopathy. Arq Bras Cardiol. 2012;98(2):111-9. [ Links ]

Gerbi FC, Takahashi JT, Cardinalli-Neto A, Nogueira PR, Bestetti RB. Heart rate variability in the frequency domain in chronic Chagas disease: correlation of autonomic dysfunction with variables of daily clinical practice. Int J Cardiol. 2011;150(3):357-8 [ Links ]

Received: November 10, 2014; Revised: January 24, 2015; Accepted: January 26, 2015

Mailing Address: Paulo Roberto Benchimol Barbosa, Universidade do Estado do Rio de Janeiro. Boulevard Vinte e Oito de Setembro, 77. Térreo. Sala da Coordenadoria de Assistência Médica, Vila Izabel. Postal Code 20551-030, Rio de Janeiro, RJ – Brazil. E-mail: pbarbosa@cardiol.br, benchimol@globo.com

Author contributions

Conception and design of the research:Nasario-Junior O, Benchimol-Barbosa PR. Acquisition of data: Pedrosa RC. Analysis and interpretation of the data: Nasario-Junior O, Benchimol-Barbosa PR, Nadal J. Statistical analysis: Nasario-Junior O, Benchimol-Barbosa PR, Nadal J. Obtaining financing: Pedrosa RC, Nadal J. Writing of the manuscript: Nasario-Junior O, Benchimol-Barbosa PR, Pedrosa RC, Nadal J. Critical revision of the manuscript for intellectual content: Nasario-Junior O, Benchimol- Barbosa PR, Pedrosa RC, Nadal J. Supervision / as the major investigador:Nadal J.

Potential Conflict of Interest

No potential conflict of interest relevant to this article was reported.

Creative Commons License This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.