Research Article
Volume 1 Issue 1 - 2015
Beat-To-Beat Ventricular Repolarization Duration Variability Assessed By Cardiac Acceleration and Deceleration Phases in Athletes
O Nasario-Junior¹, PR Benchimol-Barbosa¹²* and J Nadal¹
1Programme of Biomedical Engineering, COPPE, Federal University of Rio de Janeiro, Brazil
2University Hospital Pedro Ernesto, State University of Rio de Janeiro, Brazil
*Corresponding Author: Paulo Roberto Benchimol Barbosa, University Hospital Pedro Ernesto, Rio de Janeiro State University, 77 Board of Directors Suite, Rio de Janeiro, 20551-030, Brazil.
Received: July 16, 2015; Published: July 31, 2015
Citation: Olivasse Nasario-Junior., et al. “Beat-To-Beat Ventricular Repolarization Duration Variability Assessed By Cardiac Acceleration and Deceleration Phases in Athletes”. EC Cardiology 1.1 (2015): 33-42.
Dynamic ventricular repolarization duration (VRD) and RR-interval coupling relates to autonomic control and myocardial electrical stability. Phase-rectification of RR-interval series separates acceleration (AC) and deceleration (DC) phases, reflecting sympathetic and parasympathetic influences on heart rate, respectively. To assess the effect of physical conditioning status on dynamic VRD and phase-rectification-driven RR-interval coupling. Controls (n = 10) and Athletes (n = 10) groups underwent 15 min resting ECG. RR-interval histogram was calculated, with 100 ms width classes, ranging from 700 ms to 1300 ms. RR-intervals and respective VRD, defined as the segment between R-wave peak and the apex of the T-wave (RTA) were grouped. The averages of RTA (MRTA) and RR-intervals (MRR) were calculated, using the whole series, AC and DC phases, as well as root-mean-squared difference of consecutive RTA (RMS-RTA). Values were pooled, and compared inter groups and inter classes. Regression lines were calculated, and slope compared between groups (α < 0.05). MRTA was larger in Athletes than in Controls. MRTA increased proportionally to MRR in both AC and DC, and slope was steeper in Athletes. MRTA slopes in AC and DC phases did show differences, in both groups. RMS-RTAAC and RMS-RTADC showed no intergroup differences. An inverse correlation between RMS-RTA and MRR was found only in Athletes, similarly in both phases. In athletes, VRD is larger, for all RR-interval durations, and RTA slope steeper than in heathy sedentary, indicating faster adaptation. In athletes, VRD variability decreases as RR-interval increases, indicating a beneficial effect of fitness on repolarization stability.
Keywords: Ventricular repolarization duration; Cardiac cycle length; RR-interval histogram; Heart rate adaptation; Phase rectified RR-interval
Abbreviations: ECG: electrocardiogram; VRD: ventricular repolarization duration; AC: acceleration; DC: deceleration; RR: interval segment duration between consecutive R-wave peaks; RTA: segment between R-wave peak and the apex of the T-wave; MRTA: averages of RTA; MRR: averages of RR-intervals; RMS:  root-mean-squared; HR: heart rates; ECG: electrocardiogram; VO2MAX: maximal oxygen consumption; METs: metabolic equivalents; SD: standard deviation; HRV: heart rate variability; PRSA: phase-rectified signal averaging
Regular exercise leads to structural and functional adaptations that improve cardiac function as a consequence of increased demand on the cardiovascular system [1,2]. Among them, are i) increases in left ventricular mass and volume [3], ii) reduced resting heart rates (HR) induced by increased vagal tone [4,5] and, iii) electrical changes, characterized by the redistribution of electrical charges on myocardial surface, as indicated by increases in both ventricular activation amplitude and repolarization duration (QT-interval), which are observed in the resting electrocardiogram (ECG) waveforms of athletes [6,7].
The relationship between ventricular repolarization duration (VRD) and cardiac cycle length is a valuable tool to assess cardiac adaptation to autonomic input [8]. It is well known the VRD adapts to changes in HR, which makes it difficult to compare the repolarization interval for subjects with different physiological conditions. The effects of exercise training on VRD indices may have an autonomic nature, partially explained by an increase in cardiac vagal activity [9] and, additionally, have not been extensively studied.
Recently, an approximate isolation of distinct autonomic contribution on HR has been possible by assessing the capability of RR-interval series to accelerate (AC) and decelerate (DC), representing, respectively, sympathetic and parasympathetic contribution phases. To further accomplish this task, it is initially detected whether a particular RR-interval increases or decreases relatively to the previous one, and then such particular RR-intervals are separated in new series [10].
Previous studies reproduced the VRD dependence on the cardiac cycle lengths, indicating that the separation by RR-interval classes can be useful to compare different populations based on pairing the bands [11]. Additionally, depending on vagal stimulus intensity, the ascending rate of the RR-interval series (DC phase) changes accordingly, determining steepest slope variation [12]. Then, discriminating the DC and AC series, a strongest vagal stimulus would determine faster ventricular adaptations (steepest curve) and vice-versa, potentially affecting the relation between them (hysteresis). Thus, the objective of the study was: i) to develop an analysis that relates the ventricular repolarization duration and its variability to RR-intervals, stratified by RR histogram classes, comparing healthy sedentary subjects and athletes, and ii) to assess AC and DC phases of RR-interval series, discriminating sympathetic and parasympathetic effects.
Materials and Methods
Study population
The analyzed signals were extracted from an existing high resolution ECG database as described previously [5]. The study protocol was approved by Instituto Nacional de Cardiologia Ethics Committee and informed consent was obtained from each volunteer. Ten elite runners ([mean ± SD] 8.9 ± 3.2 years of training; six to eight training sessions/week; 90 to 120 min/session; 90 to 110 km/week) were enrolled (Athlete group). A group of 10 healthy sedentary volunteers were included as control (Control group). Inclusion criteria, physical assessment procedures and maximal oxygen consumption (VO2MAX) estimation protocol have been published elsewhere [5].
VO2MAX was divided by the constant 3.5 mL kg-1 min-1 to be converted into metabolic equivalents (METs). Control and Athlete groups were separated according to estimated VO2MAX, by arbitrarily defining as less than 11.5 METs for sedentary controls and more than 16.0 METs for athletes.
Both groups were adjusted by age, gender and matched by anthropometric data (Table 1). The aim of anthropometric data intergroup matching was to minimize inter-group physiological and anthropometric variability, thus reducing potential effect of thoracic geometry on surface ECG signals.
  Controls Athletes
Age (years) 29.0 ± 5.4 24.4 ± 7.2
BMI (Kg/m2) 23.8 ± 3.8 20.7 ± 1.9
BSA (m2) 1.8 ± 0.2 1.8 ± 0.2
APTD (cm) 21.3 ± 1.9 21.1 ± 1.2
LLTD (cm) 28.1 ± 3.2 28.0 ± 1.2
METs 8.7 ± 1.9 19.6 ± 1.3*
Table 1: Anthropometric and demographic characteristics (mean ± SD) of the subjects who participated in the study. BMI: body mass index; BSA: body surface area; APTD: anteroposterior thoracic diameter; LLTD: laterolateral thoracic diameter; METs: metabolic equivalents *p = 0.001.
Signal acquisition and processing
The high resolution ECG signals were acquired using modified bipolar Frank XYZ orthogonal leads shortly after application of the questionnaire and physical examination. Before a 15 min continuous signal acquisition, subjects remained in the supine position for 5 min for stabilization of autonomic modulation after the change from the orthostatic position. Detailed description of the equipment was previously described and digital data were processed with custom-made pattern recognition software [13].
Wave detection
The analysis of the RR-interval length was carried out after detection of the QRS complex. Arti-facts and ectopic beats were excluded by correlation, precocity and visual inspection, by one expert.
The analysis of the VRD was carried out with the signal low-pass filtered at 15 Hz (Butter-worth, 2nd order). The distance between the top of the QRS complex (R) and the apex of the T wave (TA) in normal beats defined RTA interval, which was employed in a sole purpose of analyzing repolarization adaptation over instantaneous cardiac cycle [11]. The R point was defined as the absolute maximum of the R-wave, as well as the apex of the T-wave (TA, Figure 1). The RR- and RTA-intervals were analyzed on X lead.
Figure 1: Identification of the apex (point) on R- and T-waves, which allowed precise identification of the ventricular repolarization duration by RTA interval.
Instantaneous RR and RTA interval analysis
The histogram was constructed for each individual RR-interval series, and divided into classes of 100 ms width, ranging from 700 ms to 1300 ms, which represents a variation between 46 and 100 bpm in HR. For each histogram class, and respective to each RR-interval series, it was calculated mean (MRR), standard deviation (SDRR) of consecutive normal RR-intervals, and mean (MRTA), standard deviation (SDRTA) and root-mean-squared difference (RMS-RTA) of consecutive normal RTA intervals. Only pairs of consecutive normal RR and RTA intervals for individual series that lied inside a particular class of the RR histogram were analyzed together.
For a particular histogram class (class) of the ith series, containing Ni, class RR-intervals, the calculus of the mean (Mxi, class), standard deviation (SDxi, class), root-mean-squared difference (RMSxi, class) of the normal RR and RTA intervals was performed as follows:
where x represents either RR or RTA interval.
For each histogram, classes with 20 or less intervals were excluded of analysis to avoid bias due to lack of statistical precision.
The values of the variables Mxi, class, SDxi, class and RMSxi, class were aggregated to the respective histogram class. The pooled mean (Mx class), standard deviation (SDx class) and root-mean-squared difference (RMSx class) of RR and RTA intervals for each histogram class, weighted by respective degree-of-freedom (ηi, class), were calculated according to:
where x represents either RR or RTA interval.
The variables MRTA class and RMS-RTA class were plotted and correlated with MRR class.
Instantaneous AC and DC analysis
RR-interval histograms in AC and in DC phases were also built, following the rules described above. RR-interval in AC (RRAC) and in DC (RRDC) phases was classified accordingly. To further accomplish this task, it was initially isolated the data points as either acceleration (AC) or deceleration (DC) phases. If a particular RR-interval increased relatively to the previous one, a DC interval occurred. As the instantaneous RR-interval increased, it characterized parasympathetic input (DC; lozenge symbols in Figure 2). Conversely, a sympathetic effect on the cardiac cycle length was represented whenever the RR-interval decreased relatively to the previous one, and AC interval was defined (AC; represented by circle symbols in Figure 2). After RR-intervals classification, RTA intervals histograms were built, respectively, following the correspondent RR-intervals phases: RTAAC derived from RRAC and RTADC from RRDC intervals.
Figure 2: Acceleration (RRAC) and Deceleration (RRDC) anchor points are represented in RR-intervals samples derived from an ECG recording. RR-interval histogram is represented on the right.
Statistical analysis
RTA and RR of each subject were pooled and averaged in a class-by-class basis in Controls and Athletes groups, and compared intergroup by non-paired Student t-test. Kruskal-Wallis ANOVA was employed to compare VRD inter classes. The RTA length and its variability (MRTA and RMS-RTA) was analyzed employing all beats as well as beats selected from AC and DC phases, MRTAAC, MRTA DC, RMS-RTAAC and RMS-RTADC, respectively. Regression lines of MRR vs. MRTA, and RMS-RTA, were analyzed and angular coefficients (slope) compared between Controls and Athletes groups using non-paired Student t-test. Correlation coefficients (r) were assessed by Pearson correlation tests. All tests considered the significance level α < 0.05.
Linear correlation coefficient (r) and respective angular coefficient (slope) of regression lines between MRR and other each pooled MRTA and RMS-RTA variables in both AC and DC phases are presented in Table 2. The r values were significant for all regression lines except in Control group for RMS-RTA. No intergroup slopes differences were observed for all comparisons (p > 0.05).
    MRR vs. MRTA MRR vs. RMS-RTA
    Control Athletes Control Athletes
AC r 0.98* 0.99* -0.09 -0.95*
slope 0.0933 0.1112 -0.0007 -0.0075
DC r 0.98* 0.99* -0.32 -0.97*
slope 0.0981 0.1169 -0.0016 -0.0053
Table 2: Correlation and slopes of pooled mean RR-interval (MRR) vs. pooled mean ventricular repolarization parameters (MRTA and RMS-RTA) in AC and DC phases:
* p < 0.05, RMS: pooled root-mean-squared.
The pooled MRTAAC and MRTADC were presented for each group, respectively, in figure 3 (a) and (b), as a function of pooled MRR. MRTA showed significant difference between groups for all MRR classes (p < 0.05).
Figure 3: Pooled MRTA phase analyses (AC and DC) for (a) Control and (b) Athletes groups as a function of RR-intervals. Note that slopes in Athletes group are steeper than in Control group in AC and DC phases.
The MRTA showed no significant intra-group differences between AC and DC phase, in all MRR classes (p > 0.05). The interclasses comparison of MRTA variables showed significant difference in both groups (p < 0.05, Figure 3 (a) and (b)).
The pooled RMS-RTAAC and RMS-RTADC were presented for Athletes group in figure 4, as a function of MRR. The slopes showed no significant difference between AC and DC phases (p > 0.05). In Control group, no significant linear correlation (r) between MRR and RMS-MRTA was found.
Figure 4: Pooled RMS-RTAACC and RMS-RTADC as a function of of RR-intervals and respective slopes in Athletes groups. Linear regression lines in AC and DC phases indicate an inverse dependence of RTA variability vs. RR-interval duration.
Based on the total number of RR-intervals suiting a particular histogram class, the percent value (mean ± SD) of RR-intervals pairs rejected as either one not pertaining to the same histogram class was 30.9% ± 10.5% for the Control group and 50.5% ± 16.5% for Athletes. Figure 5 shows RR-interval pairs histograms for each group.
Figure 5: Histograms of RR-interval-pairs analyzed for Control and Athletes groups. (see text for details)
This study applied a computerized method to automatically analyze the relation between VRD and cardiac cycle length in high performance athletes and healthy sedentary controls. Utilization of RTA interval as a measure of VRD instead of the conventional QT interval has been proved to be feasible and has several advantages [14,15]. Most important, RTA is easily identified in the majority of the cases because it is limited by two sharp edges of the ECG (the R- and the T-wave apexes). The conventional QT interval requires accurate identification of the Q-wave and the T-wave offset, and the latter is subject to controversies related to optimal definition of reference offset point in visual identification processes and to adequacy of automated computer algorithms [14]. The increased accuracy in the VRD measurement based on RTA definition, on the other hand, allows the application of non-interactive software [14]. Furthermore, it has been demonstrated that most of both VRD variability and coherence to RR-interval series is concentrated in the first portion of the QT interval (ending at T-wave apex) [16]. In fact, recent studies indicate that VRD offset assessed either at the peak of the T-wave or the inflexion point after the peak show high correlation with the VRD measured at end of the T-wave, validating this measurement for more accurate determination of ventricular variability [15,17]. The RTA calculation by adjusting the parabola to determine the peak of the T-wave has been previously presented and shown to preserve VRD variability accuracy [11].
The QT interval reflects the time window extending from the onset of the ventricular activation to the offset of repolarization. Particularly, abnormal repolarization syndromes are often expressed as extreme T-wave form variation and/or prolongation and all of them are associated with life-threatening heart rhythm disturbances [17,18]. However, the determination of abnormal QT interval is challenging due to the frequent lack of differentiation of the end of the T-wave. Furthermore, it undergoes dynamic variations depending on age, instantaneous HR, autonomic status, medications, etc [17].
In both athletes and healthy sedentary subjects, QT interval duration shows significant dependence on the corresponding cardiac cycle length [8]. Additionally, the autonomic nervous system, by vagal tonus predominance (parasympathetic), may also cause QT interval to prolong, and increase its temporal dispersion in well-conditioned subjects [9]. In elite athletes, besides the vagal tonus predominance and, consequently, resting bradycardia, which increase the absolute values of QT interval duration [19], an increase in left ventricular mass is considered to be an otherwise benign physiological phenomenon, also known as “athlete’s heart”. Thus, an isolated slightly prolonged QT interval in athletes may reflect delayed repolarization as a result of increased ventricular wall thickness [20] and/or slow heart rates [21]. Thus, the analysis of autonomic contributions to VRD dependence may be a valuable tool to assess VRD adaptation to cardiac cycle length in this population.
Because of the substantial inter-subject variability of the QT/RR-interval ratio, no mathematical formula holds true for a definite and accurate correction regarding HR status. Consequently, a RR-interval correction formula that performs well in one subject may overcorrect or under correct the QT interval in other [21]. Hence, the present study relates VRDs to respective RR-intervals, preserving the first from correction formulas and allowing more unbiased comparison between healthy subjects and athletes.
The behavior of VRD was analyzed by grouping RTA interval lengths according to the histogram classes of normal RR-intervals. This procedure made it possible to cluster beats expected to bear similar influences due to instantaneous time factors. Additionally, RR series were appropriately isolated of distinguish autonomic contribution on HR variability (HRV), by assessing the capability of the heart to accelerate or to decelerate, respectively, sympathetic (AC phase) and parasympathetic (DC phase) contributions.
The RR-interval phase-based processing has been successfully employed in previous studies to assess cardiac vagal modulation by phase-rectified signal averaging (PRSA) procedure [10,12]. The technique has been shown to be effective in distinguishing athletes from sedentary healthy volunteers. It hypothesized that depending on vagal stimulus intensity, the rate of ascends of the RR-interval series would change accordingly, determining slope variation. Thus, a strongest vagal stimulus would determine a steepest slope and vice-versa, affecting the DC measure.
In both groups, the mean VRD measures were strongly dependent on the instantaneous RR-interval, confirming previous findings [11,22]. In a physiological range of variability (700 to 1300 ms), pooled MRTA are greater at larger MRR intervals (Figure 3). This relation held a strong linear dependence (r > 0.98; Table 2). MRTA intra group comparison between AC and DC phases showed no significant differences either in Controls or in Athletes. Thus, it was not possible to isolate any potential hysteresis in VRD adaptation using histogram approach, in the RR-interval range analyzed, indicating that beats under a particular RR-interval class showed similar repolarization duration regardless cardiac phase. Because AC phase corresponded to sympathetic and DC phase to parasympathetic inputs of the RR-interval, based on current data, autonomic phase did not significantly impact repolarization duration in addition to RR-interval class itself, in the analyzed range.
On the other hand, beat-to-beat VRD variability has been considered a measure of ventricular autonomic modulation and has prognostic implications [22-25]. In the present study, VRD variability was assessed by root-mean-squared of consecutive RTA intervals (RMS-RTA) in AC and DC phases using the RR-interval histogram approach. In athletes group, RMS-RTA showed an inverse linear relation with RR-interval class, in both phases, evidencing that VRD variability increased as RR-interval decreased and vice-versa (Figure 4). Although this inverse relation has been previously reported by Barbosa,. et al [11], to the best of our knowledge, this is the first report of the phenomenon in high performance athletes. Although the interpretation of those observations may seem elusive, reduction of VRD variability as RR-interval increases clearly points to a beneficial effect of physical conditioning on ventricular repolarisation stabilization. Those observations were not clearly evident among Controls (Data not shown).
RR-interval pairs in both groups (Figure 5) were frequently placed in classes ranging from 800 to 1100 ms, allowing appropriate class-by-class comparison. Additionally, a larger total number of RR-interval pairs were rejected in Athletes than in Controls, as consecutive intervals not pertaining to the same class were expected to occur due to larger inter-cycle length variation in the formers.
Study limitations include:
  1. Small sample size,
  2. Two physiologically well-defined groups,
  3. VO2MAX was estimated indirectly,
  4. Groups were loosely matched by age.
It should be emphasized that the present method has not been tested as a risk stratification tool for clinical conditions related to arrhythmias, particularly in athletes. The ability of the method to assess the RR-interval to VRD relationship needs further assessments in different clinical settings.
Using RR-interval histogram-based approach, VRD shows linear dependence on RR-interval in both healthy sedentary and high performance athletes. This dependence shows no remarkable differences between acceleration and deceleration phases.
 Additionally, VRD in athletes is slightly larger than in healthy sedentary volunteers for equivalent RR-interval classes. Among athletes, beat-to-beat VRD variability shows an inverse relation to RR-interval duration, indicating a potential beneficial of physical fitness on ventricular repolarisation stabilization.
This work was partially supported by the Brazilian Research Council (CNPq/MCTI) and PROEX Grant (CAPES/MEC). Author wish to acknowledge also the contribution of Moacir Marocolo Jr, DSc, in data acquisition.
Conflict of Interest
No financial interest or any conflict of interest exists.
  1. Iwasaki K., et al. “Dose-response relationship of the cardiovascular adaptation to endurance training in healthy adults: how much training for what benefit?” Journal of Applied Physiology 95.4 (2003): 1575-1583.
  2. Atchley AE Jr and Douglas PS. “Left ventricular hypertrophy in athletes: morphologic features and clinical correlates”. Cardiology Clinics 25.3 (2007): 371-382.
  3. Maskhulia L., et al. “Left ventricular morphological changes due to vigorous physical activity in highly trained football players and wrestlers: relationship with aerobic capacity”. Georgian Medical News 133 (2006): 68-71.
  4. Melanson EL and Freedson PS. “The effect of endurance training on resting heart rate variability in sedentary adult males”. European Journal of Applied Physiology 85.5 (2001): 442-449.
  5. Marocolo M., et al. “The effect of an aerobic training program on the electrical remodeling of heart high-frequency components of the signal-averaged electrocardiogram is a predictor of the maximal aerobic power”. Brazilian Journal Of Medical and Biological Research 40.2 (2007):199-208.
  6. Nasario-Junior O., et al. “Effect of aerobic conditioning on ventricular activation: a principal components analysis approach to high-resolution electrocardiogram”. Computers in Biology and Medicine 43.11 (2013):1920-1926.
  7. Barbosa EC., et al. “Ionic mechanisms and vectorial model of early repolarization pattern in the surface electrocardiogram of the athlete”. Annals of Noninvasive Electrocardiology 13.3 (2008): 301-307.
  8. Haigney MC., et al. “QT interval variability and spontaneous ventricular tachycardia or fibrillation in the Multicenter Automatic Defibrillator Implantation Trial (MADIT) II patients”. Journal of the American College of Cardiology 44.7 (2004): 1481-1487.
  9. Lutfullin IY., et al. “A 24-hour ambulatory ECG monitoring in assessment of QT interval duration and dispersion in rowers with physiological myocardial hypertrophy”. Biology of Sport 30.4 (2013): 237-241.
  10. Bauer A., et al. “Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study”. Lancet 367.9523 (2006): 1674-1681.
  11. Benchimol-Barbosa PR., et al. “The Effect of the Instantaneous RR Interval on the Dynamic Properties of the Heart Rate and the Ventricular Repolarization Duration Variability”. CinC (2000): 821-824.
  12. Nasario-Junior O., et al. “Refining the deceleration capacity index in phase-rectified signal averaging to assess physical conditioning level”. Journal of Electrocardiology 47.3 (2014): 306-310.
  13. Barbosa PR., et al. “Reduction of electromyographic noise in the signal-averaged electrocardiogram by spectral decomposition”. Biomedical Engineering, IEEE Transactions 50.1 (2003): 114-117.
  14. Merri M., et al. “Relation between ventricular repolarization duration and cardiac cycle length during 24-hour Holter recordings. Findings in normal patients and patients with long QT syndrome”. Circulation 85.5 (1992): 1816-1821.
  15. Porta A., et al. “RT variability unrelated to heart period and respiration progressively increases during graded head-up tilt”. American Journal of Physiology 298.5 (2010): H1406-H414.
  16. Merri M., et al. “Electrocardiographic quantitation of ventricular repolarization”. Circulation 80.5 (1989): 1301-1308.
  17. Oosterom AV. “Measuring the T Wave of the Electrocardiogram; The How and Why”. Measurement Science Review 9.3 (2009): 53-63.
  18. Viskin S. “Long QT syndromes and torsade de pointes”. Lancet 354.9190 (1999): 1625-1633.
  19. Lecocq B., et al. “Physiologic relation between cardiac cycle and QT duration in healthy volunteers”. American Journal of Cardiology 64.8 (1989): 481-486.
  20. Tanriverdi H., et al. “QT dispersion and left ventricular hypertrophy in athletes: relationship with angiotensin-converting enzyme I/D polymorphism”. Acta Cardiologica 60.4 (2005): 387-393.
  21. Malik M., et al. “Relation between QT and RR-intervals is highly individual among healthy subjects: implications for heart rate correction of the QT interval”. Heart 87.3 (2002): 220-228.
  22. Berger RD., et al. “Beat-to-beat QT interval variability: novel evidence for repolarization lability in ischemic and nonischemic dilated cardiomyopathy”. Circulation 96.5 (1997): 1557-1565.
  23. Haigney MC., et al. “QT interval variability and spontaneous ventricular tachycardia or fibrillation in the Multicenter Automatic Defibrillator Implantation Trial (MADIT) II patients”. Journal of the American College of Cardiology 44.7 (2004): 1481-1487.
  24. Wellens HJ., et al. “Risk stratification for sudden cardiac death: current status and challenges for the future”. European Heart Journal 35.25 (2014): 1642-1651.
  25. Bari V., et al. “Multiscale complexity analysis of the cardiac control identifies asymptomatic and symptomatic patients in long QT syndrome type 1”. Plos One 9.4 (2014): e93808.
Copyright: © 2015 Olivasse Nasario-Junior., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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PMCID: PMC6604646

EC Anaesthesia
Real Time Locating Systems and sustainability of Perioperative Efficiency of Anesthesiologists.

PMID: 31406965 [PubMed]

PMCID: PMC6690616

EC Pharmacology and Toxicology
A Pilot STEM Curriculum Designed to Teach High School Students Concepts in Biochemical Engineering and Pharmacology.

PMID: 31517314 [PubMed]

PMCID: PMC6741290

EC Pharmacology and Toxicology
Toxic Mechanisms Underlying Motor Activity Changes Induced by a Mixture of Lead, Arsenic and Manganese.

PMID: 31633124 [PubMed]

PMCID: PMC6800226

EC Neurology
Research Volunteers' Attitudes Toward Chronic Fatigue Syndrome and Myalgic Encephalomyelitis.

PMID: 29662969 [PubMed]

PMCID: PMC5898812

EC Pharmacology and Toxicology
Hyperbaric Oxygen Therapy for Alzheimer's Disease.

PMID: 30215058 [PubMed]

PMCID: PMC6133268

News and Events

November Issue Release

We always feel pleasure to share our updates with you all. Here, notifying you that we have successfully released the November issue of respective journals and the latest articles can be viewed on the current issue pages.

Submission Deadline for Upcoming Issue

ECronicon delightfully welcomes all the authors around the globe for effective collaboration with an article submission for the upcoming issue of respective journals. Submissions are accepted on/before December 13, 2022.

Certificate of Publication

ECronicon honors with a "Publication Certificate" to the corresponding author by including the names of co-authors as a token of appreciation for publishing the work with our respective journals.

Best Article of the Issue

Editors of respective journals will always be very much interested in electing one Best Article after each issue release. The authors of the selected article will be honored with a "Best Article of the Issue" certificate.

Certifying for Review

ECronicon certifies the Editors for their first review done towards the assigned article of the respective journals.

Latest Articles

The latest articles will be updated immediately on the articles in press page of the respective journals.