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Article

Heart Rate Asymmetry in Healthy Children

by
Dominika Zalas
1,
Waldemar Bobkowski
1,
Jarosław Piskorski
2 and
Przemysław Guzik
3,*
1
Department of Pediatric Cardiology, Poznan University of Medical Sciences, 61-701 Poznań, Poland
2
Institute of Physics, University of Zielona Gora, 65-516 Zielona Góra, Poland
3
Department of Cardiology-Intensive Therapy, Poznan University of Medical Sciences, 61-701 Poznań, Poland
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2023, 12(3), 1194; https://doi.org/10.3390/jcm12031194
Submission received: 6 December 2022 / Revised: 25 January 2023 / Accepted: 31 January 2023 / Published: 2 February 2023
(This article belongs to the Section Cardiovascular Medicine)
Browse Figure
Versions Notes

Abstract

:
Heart rate asymmetry (HRA) is a physiological phenomenon characterized by an unequal contribution of heart rate decelerations and accelerations to different heart rate variability (HRV) features. While HRA has been demonstrated in adults’ ECGs of different duration, a similar investigation in healthy children has not been conducted. This study investigated the variance- and number-based HRA features in 96 healthy children (50 girls and 46 boys, aged 3–18 years) using 24-h ECGs. Additionally, we studied sex differences in HRA. To quantify HRA, variance-based and relative contributions of heart rate decelerations to short-term (C1d), long-term (C2d), and total (CTd) HRV, and the number of all heartbeats (Nd) were computed. Heart rate decelerations contributed more to C1d, but less to C2d and CTd, and were less frequent than heart rate accelerations. Short-term HRA was better expressed in boys. The majority of children (93.7%) had short-term HRA, 88.5% had long-term HRA, 88.5% had total HRA, and 99.0% had more accelerations than decelerations. No sex differences were observed for the rate of various HRA features. Heart rate asymmetry is a common phenomenon in healthy children, as observed in 24-h ECGs. Our findings can be used as reference data for future clinical studies on HRA in children.

1. Introduction

Physiologically, the heart rate (HR) originates in the sinus node [1]. The frequency of its spontaneous depolarizations constantly fluctuates [2] due to various physical factors, such as temperature [3,4,5,6], and chemical factors, such as the concentration of electrolytes and hypoxia [7,8,9]. It is speculated that biological factors, such as sympathetic and parasympathetic activity, neurotransmitters, hormones, and cytokines, may also contribute to instantaneous HR [10,11,12,13].
The cardiac cycle duration is measured as the time between two consecutive R waves on an ECG (RR interval). Changes in HR can either be accelerations or decelerations. For RR intervals, these changes are either shortenings or prolongations, respectively. If the changes are of sinus rhythm origin, they are responsible for a physiological phenomenon known as heart rate variability (HRV) [14,15].
Different methods quantify HRV, e.g., variance-based methods (Poincaré plots, spectral analysis using Fast Fourier transformation, or Scargle–Lomb periodograms), entropy and detrended fluctuation analysis, or heart rate turbulence [16,17,18,19,20]. HRV has been chiefly used for two primary purposes: as an indirect measure of the autonomic control of the HR and as a predictor of clinical outcomes in various groups of patients.
Several HRV indices have been believed to be an indirect, non-invasive, and rough approximation of HR autonomic modulation, specifically sinus node activity. However, the 1996 Task Force document on HRV standards suggested that only short recordings of, for example, a 5-min duration in specific stationary conditions could be used for this purpose [21]. It has been debated and criticized, with some questioning whether HRV is a good proxy of the sympathovagal balance or vagal tone [22,23]. Recently, Marmerstein et al., based on an interesting animal study in rats, provided evidence that HRV parameters, which were assumed to reflect the vagal tone, are not correlated with vagal activity at all [24].
For longer ECGs, lasting hours to days, the original Task Force document on HRV has pointed out that HRV should not be employed to evaluate HR autonomic regulation [21]. HRV indices are measured globally for longer ECG recordings, such as 24-h, and have been documented only for risk prediction in various groups of patients. Some examples are adults who have survived myocardial infarction or have heart failure [25].
HRV has also been studied in various clinical and physiological studies. In adults, HRV indices change in different clinical conditions, such as acute coronary syndrome, heart failure, diabetes mellitus, hypertension, Parkinson’s disease [25,26,27], or physiological challenges—during exercise [28,29], orthostatic challenge [30,31], mental testing [32], and controlled breathing [33]. HRV has also been analyzed in healthy and ill children, for example, with congenital heart defects [34], diabetes mellitus [35,36], attention-deficit hyperactivity disorder (ADHD) [37], or after cardiac surgery [38]. Yet, HRV has never been systematically studied in healthy kids.
Physiologically, HRV changes with age [14,39,40,41,42,43,44,45,46,47,48,49,50]. Generally, many HRV indices increase during childhood and adolescence until approximately 20 years of age [46,51]. In adults, they gradually decline with age [52,53,54]. Some sex differences in HRV have also been demonstrated. For example, the standard deviation of the projection of the HRV Poincaré plot on the line of identity (SD2) was found to be higher in adult males [55,56]. However, the comparisons of HRV between girls and boys [39,40,41,42,43,44,45,46,47,48,51,57,58,59] show mixed results that sex differences may or may not be present in children.
As mentioned, HR accelerations and decelerations represent all RR changes creating HRV. It appears that HR accelerations and decelerations have an unequal input to short-term, long-term, and total HRV, and this physiological phenomenon is termed heart rate asymmetry (HRA) [18,19,30,60].
Our group showed in 2006 [18] and later [19,60,61,62,63] that HR decelerations contribute more to the short-term and less to the long-term and total HRV in ECG recordings of different lengths in adults [18,19,60,61,62,63]. Additionally, a phenomenon of HRA compensation exists—a larger input of HR decelerations to the short-term HRV compensates for a more considerable contribution of HR accelerations to the long-term HRV [61]. Most HRA investigations have been performed in shorter ECGs of 1- to 30-min duration and only a few on longer ECGs of 24-h duration [63,64,65].
It appears, however, that none of the HRA studies, including its compensation, were performed on the 24-h ECGs in healthy children. We assume that these two phenomena are present in healthy children. In the past, sex differences have been investigated in children only in HRV but never in HRA.
We hypothesize that differences in the expression of HRA can be found in healthy children in long ECGs. Thus, we aimed to: (1) analyze the presence and expression of the short-, long-term, and total HRA in 24-h Holter ECGs collected from healthy children; (2) study the presence of HRA compensation in children; (3) compare the expression and rate of HRA and HRA compensation between girls and boys; and (4) explore the association between the age and HRA.

2. Materials and Methods

The Ethics Committee approved the study at Poznań University of Medical Sciences (file 549/10, appendix 351/19). Written informed consent was obtained from the parents or legal guardians of all children and participating children above seven years of age. Younger children who could not write gave oral consent in the presence of their parent or legal guardian.

2.1. Study Group

During 2017–2020, 1600 children were referred to our Out-patient Pediatric Cardiology Department for various reasons, such as heart murmur, impaired exercise tolerance, fainting, heart palpitations, and chest pain. These children underwent a thorough cardiac examination, which included medical history, physical examination, 12-lead ECG, and transthoracic echocardiography.
For our study, 100 healthy subjects were selected, and the remaining 1500 children were ruled out based on the exclusion criteria: chronic disease, taking medications regularly, abnormalities on physical examination—except for the innocent heart murmur (confirmed on echocardiography), abnormal body mass index (BMI), laboratory measurements, blood pressure; any deviation from normal sinus rhythm on ECG, and abnormalities on echocardiography (such as valvular insufficiency or stenosis, septal defects, decreased myocardial contractility). Additionally, all children who performed professional sports or presented symptoms of infection four weeks prior to the visit were excluded as well.
Although all children were referred to our Out-patient Pediatric Cardiology Department for potentially cardiovascular reasons, those reasons are also everyday problems in pediatric practice, not necessarily linked to an organic background. The most common example for referral is innocent heart murmurs which may be observed due to hyperkinetic circulation in healthy children, a poor physical condition may occur due to a sedentary lifestyle, syncope after prolonged standing or insufficient fluid consumption, heart palpitations, or chest pain of psychogenic background in stressful situations.
The selected 100 children without any medical conditions underwent 24-h Holter ECG monitoring as the second step. Further exclusion criteria were a substantial number of supraventricular and ventricular extrasystoles (at least 100 over one hour), pairs or tachycardias, pathological bradycardia, non-sinus rhythm, atrioventricular conduction disturbances, or less than 90% of sinus rhythm on 24-h ECG Holter monitoring. The final number of included healthy children was 96.

2.2. 24-Hour ECG Holter Monitoring

Schiller’s Medilog® Darwin 2 (Schiller, Switzerland) 3-channel ECG recorder with a sampling frequency of 1000 Hz was used for the 24-h ECG Holter monitoring. The ECG recordings were visually verified for beat misclassifications and manually corrected if needed. The minimum recording length was 18 h, and all recordings included night hours. The recordings were exported into text files of RR intervals for further calculations, and annotations about the beat type were added (sinus, supraventricular, ventricular, technical artifact).

2.3. Heart Rate Asymmetry Analysis

For HRA analysis, we used the free-of-charge HRAExplorer software developed by Piskorski and Guzik [19], which is available at https://hraexplorer.com (accessed on 1 December 2022). All codes are available at https://github.com/jaropis/HRAexplorer (accessed on 1 December 2022). For more details, please refer to Piskorski and Guzik [19]. HRA analysis was based on the method of Poincaré plot of RR intervals (derived from ECG), as proposed by Guzik and Piskorski [19] (Figure 1), where the x-axis represents the current RR interval (RRn), and the y-axis represents the successive RR interval (RRn+1). The identity line represents points with the same current and successive RR intervals’ duration. Points above the identity line present HR decelerations, whereas points below it represent HR accelerations. SD12 is the variance of the projection of points along the identity line, SD22 is the variance of the projection of points perpendicular to the identity line, and SDNN2 is the variance of the projection of points on the RRn axis.
Short-term HRV is measured by SD1, which represents the instantaneous beat-to-beat variability. It can be divided into parts related to HR decelerations and accelerations [66]:
SD 1 2 =   SD 1 d 2 + SD 1 a 2
SD12—variance of the dispersion of points in the Poincaré plot of RR intervals across the identity line.
SD1d2—part of SD12 related to HR decelerations.
SD1a2—part of SD12 related to HR accelerations.
For short-term HRA, the contribution of HR decelerations (C1d) is measured as follows:
C 1 d = SD 1 d 2 SD 1 2
C1d—(also known as Guzik’s index [30]) contribution of HR decelerations to short-term HRV; SD12 and SD1d2—see above.
In short-term HRA contributions of HR decelerations and accelerations add up to 1:
C 1 d + C 1 a = 1
C1a—contribution of HR accelerations to short-term HRV; C1d—see above.
Long-term HRV is measured by SD2, which represents the standard deviation of long-term continuous RR intervals. It can also be divided into parts related to HR decelerations and accelerations [66]:
SD 2 2 =   SD 2 d 2 + SD 2 a 2
SD22—variance of the dispersion of points in the Poincaré plots of RR intervals along the identity line.
SD2d2—part of SD22 related to HR decelerations.
SD2a2—part of SD22 related to HR accelerations.
For long-term HRA, the contribution of HR decelerations (C2d) is measured as follows:
C 2 d = SD 2 d 2 SD 2 2
C2d—contribution of HR decelerations to long-term HRV; SD22 and SD2d2—see above.
By analogy, in long-term HRA, contributions of HR decelerations and accelerations add up to 1:
C 2 d + C 2 a = 1
C2a—contribution of HR accelerations to long-term HRV; C2d—see above.
SDNN is the standard deviation of the duration of normal RR intervals and depends on short- and long-term HRV; it describes the total HRV [66]:
2 SDNN 2 = SD 1 2 +   SD 2 2
SDNN2—variance of normal-to-normal RR intervals.
SDNNd2—part of SDNN2 related to HR decelerations.
SDNNa2—part of SDNN2 related to HR accelerations.
The total HRA is analyzed as follows:
CTd = SDNNd 2 SDNN 2
CTd—contribution of HR decelerations to total HRV; SDNNd2, SDNN2—see above.
Analogically, in total HRA contributions of HR decelerations and accelerations sum up to 1:
CTd + CTa = 1
CTa—contribution of HR accelerations to total HRV; CTd—see above.
For the analysis of the number of HR decelerations and accelerations, the parameter Nd was used. Its mathematical formula is as follows:
Nd = nd nd + na
Nd—contribution of HR decelerations to the total number of RR intervals (also called Porta’s index).
nd—the absolute number of HR decelerations, na—the absolute number of HR accelerations.
The short-term HRA (HRA1) is present when C1d > 0.5, the long-term HRA (HRA2) exists when C2d < 0.5, and the total HRA (HRAT) is observed when CTd < 0.5. The HRA compensation (HRAcomp) phenomenon occurs when both C1d > 0.5 and C2d < 0.5 are present. Additionally, the HRA of the number of HR decelerations and accelerations (HRAN) is present when Nd < 0.5.

2.4. Statistical Analysis

The data distribution in most continuous parameters was not normal (Shapiro–Wilk test). Therefore, the nonparametric Mann–Whitney test was used, and the data were summarized as a median value, 25th, and 75th percentile (IQR—interquartile range). The comparison of HRA parameters between girls and boys was performed using the Mann–Whitney test. The occurrence rate of various types of HRA was checked using the binomial test, and the differences in the rates of HRA between girls and boys were examined with Fisher’s test. The correlation between age and continuous HRA variables was performed using the nonparametric Spearman correlation analysis. A p-value of <0.05 was considered statistically significant. All analyses were performed using MedCalc Statistical Software (MedCalc Software bv, Ostend, Belgium, https://www.medcalc.org; 2020, accessed on 1 December 2022).

3. Results

Ninety-six children aged 3 to 18 years participated in the study. The median age was 14 years, and 50 participants were girls. Table 1 summarizes the HRA results for all the studied children, and Table 2 presents the prevalence of different HRA forms and HRA compensation.
The prevalence of all types of heart rate asymmetry (HRA) exceeds 88% in all children, which is the highest for HRAN. HRA1 was present in more children than HRA2 and HRAT. All children with HRA2 also had HRAcomp.
The comparison between girls and boys (Table 1) revealed that the C1d parameter was significantly higher in boys, which suggests a stronger expression of short-term HRA in boys. No differences in the prevalence of either type of HRA or the HRA compensation phenomenon were observed between the sexes (Table 2).
Nonparametric Spearman rank correlation (Table 3) showed that age was significantly and positively correlated with SD2d, SD2a, SDNNd, and SDNNa. However, the correlations were weak to moderate, with rho between 0.31 for SDNNa and 0.42 for SD2d. No other HRA descriptors were found to change with age of the studied children.

4. Discussion

Our study demonstrates that heart rate asymmetry is present in 24-h ECG recordings of healthy children. This age group strongly expresses short-term, long-term, and total HRA and HRA compensation. Sex differences exist only in the expression of short-term HRA, which is slightly stronger in boys than in girls. However, the rate of HRA and its compensation are comparable for both sexes. The variance-based measures of long-term and total HRA were found to increase with the age of the children studied.

4.1. Asymmetry of Cardiovascular Time Series

HRA is not the only asymmetrical phenomenon demonstrated in the cardiovascular time series of adults. Using the beat-to-beat finger artery pressure waveforms, we demonstrated that the number and contribution of systolic blood pressure (SBP) increases to short-term blood pressure variability (BPV) were higher than of decreases in healthy adults [67]. The short-time variabilities of various atrial to His, His to ventricles, and the atrioventricular conductions are asymmetrical in one-minute intracardiac tracings [68]. Beat-to-beat 30-min values of stroke volume (SV), cardiac output (CO), and systemic vascular resistance (SVR), recorded non-invasively by cardiac impedance, also have asymmetric properties in healthy young people in a supine position. Increases in SV, CO, and SVR values contributed more than decreases in the short-term hemodynamic variabilities of these parameters [69].
Other phenomena related to the cardiovascular system also exhibit asymmetric features. A premature ventricular contraction triggers heart rate turbulence. It begins with short-lasting, usually 2–3 beats, HR accelerations and is followed by a series of HR decelerations for up to 15–20 beats [70,71,72,73].
Another example is the baroreflex regulating HR in response to blood pressure changes. The responses of the sinus node to changes in SBP are different, with baroreflex sensitivity being larger for SBP increases [74,75,76].
Recently, we have proposed a new method for studying spontaneous baroreflex function by focusing on its asymmetric properties [77,78]. Our findings suggest that short-term, long-term, total baroreflex sensitivity and baroreflex effectiveness are asymmetric in healthy individuals resting in a supine position. Increases in SBP had a larger contribution to the short-term baroreflex sensitivity but smaller to the long-term and total baroreflex sensitivity. These opposing effects of SBP increases on short-term and long-term baroreflex sensitivity demonstrate the existence of a compensatory phenomenon in the baroreflex function.

4.2. Heart Rate Asymmetry in Children and Fetuses

Based on the studies of HRA in fetuses and neonates [79,80,81,82], it appears that HRA is already present prenatally and in the early postnatal period. A study of fetuses between 16 and 41 weeks of gestation showed the presence of short-term HRA in 95% of examined fetuses [79]. With the duration of pregnancy, no differences in C1d were observed [79]. The Nd index was smaller than 0.5 in 95% of analyzed fetuses and decreased with the duration of pregnancy.
In another study of 44 fetuses during active labor (17 fetuses at 32–36 weeks of gestation and 27 at 38–40 weeks of gestation), where 10-min recordings of RR intervals were analyzed, it was proven that the short-term HRA was observed in both groups and its expression was significantly greater in the group of term fetuses [80].
Short-term HRA was also observed in healthy full-term infants, without prenatal and perinatal risk factors (at 72 h of chronological age), in ca. 25-min recordings of RR intervals. The values of C1d were different in resting conditions and under neonatal stress (heel stick blood drawing) [81].
In 70% of subjects in the neonatal group of a study by Czippelova et al. short-term HRA was present. It may suggest that HRA is a universal property even in the early stage of central nervous system maturation [82].
Few clinical studies have described HRA in children, but none have analyzed this phenomenon in 24-h ECG recordings of healthy children. In a study of 20 girls at an average age of 16.6 years with major depressive disorder (under resting conditions), it was found that their C1d was closer to 0.5, whereas the control group of 20 age-matched healthy girls had a higher C1d (>0.5) [83]. No significant differences between these groups in response to the orthostatic challenge were reported [83].
An age-matched study showed a reduction of C1d in 20 boys with ADHD at rest and in response to standing up, compared with 20 healthy boys [84]. Additionally, more reduced C1d was present in boys with ADHD after active standing [84]. In both studies, the short-term HRA was demonstrated in healthy children, and other HRA forms were not analyzed. However, the length of studied ECGs was short.
In both studies, only segments of 300 RR intervals were used from smaller groups of only boys and in a narrower age range [83,84]. In contrast, we demonstrate the presence of all HRA forms in 24-h ECGs. Furthermore, our children group was larger and in a broader age range between 3 and 18 years, and included both boys and girls.

4.3. Heart Rate Asymmetry in Clinical Conditions

To date, HRA has been the subject of multiple studies in adults. It was analyzed in both healthy individuals during physiological challenges, and in patients with various diseases.
Frank et al. analyzed 92 healthy students between 17 and 29 years of age, who were either training yoga or partaking in school sport once a week [28]. The short-term HRA (calculated from 20-min sleep recordings) increased significantly in the yoga group, from C1d = 0.48 to C1d = 0.51 [28].
ECGs of 5 min duration of adult females, who underwent tilt test, were compared between vasovagal and healthy groups [85]. The short-term HRA based on C1d was present in 31.6% of the healthy females in supine position and 63.2% in tilt, and in 56.3% of the vasovagal group in supine and 75% in tilt position. This suggests an interesting finding that the occurrence of HRA increases after tilting and it is more common in women who suffer from vasovagal syndrome [85].
Another study which examined orthostatic challenge was performed in 15 athletes aged 18–25 years and compared with 12 non-athlete individuals [86]. C1d was higher after upright standing only in athletes. Additionally, only in athletes did Nd increase in exercise as opposed to recovery [86].
Most studies have focused on short-term HRA (C1d or Guzik’s index) and the contribution of the number of decelerations to all changing beats (Porta’s index) without examining other HRA features. For example, HRA was significantly reduced in a study of 349 elderly patients (148 women) with heart failure and symptomatic aortic stenosis [87]. The reduction in HRA was correlated with the severity of heart failure according to the New York Heart Association (NYHA) classification [87]. Another smaller study analyzed 46 patients (9 women) with heart failure, and there were no significant differences in C1d between healthy subjects and patients with heart failure with the left-ventricular ejection fraction of 30% and NYHA functional class II and III [88].
Short-term HRA was reduced in adult patients with a history of diabetes (DM) type 1 for at least 25 years [65]. Based on 24-h ECG Holter monitoring, such patients had lower C1d than healthy volunteers [65]. If C1d equals or falls below 0.5, short-term HRA is lost—such findings were reported in patients with severe depression and DM type 2 [89].
Shi et al. for the first time discovered that HRA indices (C1d and Nd) were reduced in an oncologic disease—in 61 adult patients with gastric cancer [90]. Additionally, the decrease in Nd was positively correlated with the stage of cancer (as examined by the serum fibrinogen level) [90].
Treadmill training as a form of rehabilitation in post-stroke patients involves mental engagement [91]. Jelinek et al. found that HRA based on Nd parameter changes during such training, from values > 0.5, to values < 0.5., and they connected it with alterations in the ANS [91].
Similar disorders and clinical states in which HRA has been explored also affect children. However, in contrast to studies conducted in adults, there is a lack of reference data for HRA in healthy children. Our findings may serve as a database with typical values of HRA for future clinical and interventional studies, e.g., involving diet, physical training, relaxation techniques’ effects, and gaming’s impact.

4.4. Potential Mechanisms Involved in Heart Rate Asymmetry

The mechanisms of HRA remain not fully understood and are under continuous investigation, including our research group. Many potential mechanisms are potentially involved in HRA, such as those related to breathing rate and pattern, length of inspiration and expiration [33]. Other mechanisms are linked to various physiological reflexes, such as the baroreflex controlling the cardiovascular system, particularly the relationship between HR, blood pressure and vascular resistance [74].
HRA changes throughout the day and night [92], after an orthostatic challenge [93], and is a dynamic process [63]. Some studies suggest that HRA may originate from the asymmetrical behavior of other cardiovascular time series, such as SBP [67], hemodynamics [69], and atrioventricular conduction [68]. Karmakar et al. have also demonstrated that the short-term HRA is reduced after atropine infusion in healthy individuals, suggesting that vagal control of the HR might also be involved in this phenomenon [94].
In a study conducted on rats, Marmerstein et al. found no correlation between HRV indices and vagal activity [24]. Their research challenged the longstanding belief that HRV parameters can be used as a good approximation or indirect measure of autonomic control of the heart or sympathetic–parasympathetic balance. The evidence presented by Marmerstein et al. supports the positions of other prominent researchers in the field, such as D. Eckberg, G. Parati, and G. Mancia, who are cautious and critical about using HRV as a measure of sympathovagal balance [22,23]. Since there has been no demonstration that HRV depends on sympathovagal balance or vagal tone, we refrain from making such statements.

4.5. The Novelty of the Study

To date, a detailed exploration of HRA in 24-h ECGs of healthy children has not been conducted. We demonstrate the presence of HRA and HRA compensation in 24-h ECGs of healthy children and provide reference values for these phenomena in this age group. Moreover, while the expression of short-term HRA is stronger in boys than in girls, there were no differences in the prevalence of various forms of HRA and its compensation between the sexes. Finally, only variance-based long-term and total HRA significantly alter with the age of the healthy children.

4.6. Limitations of the Study

In our study, we did not consider the pubertal development of our children in order to avoid further subdivisions of our data, reduction of statistical power, and drawing unjustified conclusions. No studied children were younger than three years, as collecting reliable 24-h ECGs is challenging in this age group. We have studied HRA only in children of the Caucasian race because most of the Polish population are ethnic Poles with typical features for Slavic people. Consequently, our study group was racially homogenous, and any conclusion extrapolated to other ethnic groups should be considered uncertain. Additionally, this study is observational, and its clinical significance is unknown.

5. Conclusions

We show that HRA and its compensation are present in healthy children in 24-h ECG recordings. The various properties of HRA are found in most (at least 88%) healthy children aged 3–18 years. Differences between girls and boys were found only in short-term HRA (which is better expressed in boys), and no differences in the prevalence of HRA were found between the sexes. The long-term and total HRA develop with age.
Further research is needed to fully understand HRA’s potential mechanisms and clinical significance in children.

Author Contributions

P.G., W.B. and D.Z. conceptualized and designed the study. W.B. designed the data collection instruments and coordinated and supervised data collection. P.G. and D.Z. conducted heart rate variability and asymmetry analyses, and P.G. performed all statistical analyses. D.Z., P.G. and W.B. performed literature research and prepared the manuscript. P.G. and W.B. reviewed and revised the manuscript. J.P. developed the software necessary to compute heart rate variability and asymmetry and critically reviewed the manuscript for important intellectual content. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Poznań University of Medical Sciences (file number 549/10, file appendix number 351/19).

Informed Consent Statement

Informed consent was obtained from all subjects and subjects’ parents or guardians involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Mangoni, M.E.; Nargeot, J. Genesis and Regulation of the Heart Automaticity. Physiol. Rev. 2008, 88, 919–982. [Google Scholar] [CrossRef]
  2. Shaffer, F.; McCraty, R.; Zerr, C.L. A Healthy Heart Is Not a Metronome: An Integrative Review of the Heart’s Anatomy and Heart Rate Variability. Front. Psychol. 2014, 5, 1040. [Google Scholar] [CrossRef] [PubMed]
  3. Fatisson, J.; Oswald, V.; Lalonde, F. Influence Diagram of Physiological and Environmental Factors Affecting Heart Rate Variability: An Extended Literature Overview. Heart Int. 2016, 11, e32–e40. [Google Scholar] [CrossRef]
  4. Muslin, A.J. Chapter 37—The Pathophysiology of Heart Failure. In Muscle; Hill, J.A., Olson, E.N., Eds.; Academic Press: Boston, MA, USA, 2012; pp. 523–535. ISBN 978-0-12-381510-1. [Google Scholar]
  5. Parmley, W.W. Pathophysiology of Congestive Heart Failure. Am. J. Cardiol. 1985, 56, 7A–11A. [Google Scholar] [CrossRef] [PubMed]
  6. Thompson, M.; Thompson, L.; Reid-Chung, A. Chapter 8—Combining LORETA Z-Score Neurofeedback with Heart Rate Variability Training. In Z Score Neurofeedback; Thatcher, R.W., Lubar, J.F., Eds.; Academic Press: San Diego, CA, USA, 2015; pp. 159–188. ISBN 978-0-12-801291-8. [Google Scholar]
  7. Weiss, J.N.; Qu, Z.; Shivkumar, K. The Electrophysiology of Hypo- and Hyperkalemia. Circ. Arrhythm. Electrophysiol. 2017, 10, 21. [Google Scholar] [CrossRef]
  8. Severi, S.; Cavalcanti, S.; Mancini, E.; Santoro, A. Effect of Electrolyte and PH Changes on the Sinus Node Pacemaking in Humans. J. Electrocardiol. 2002, 35, 115–124. [Google Scholar] [CrossRef] [PubMed]
  9. Aziz, Q.; Finlay, M.; Montaigne, D.; Ojake, L.; Li, Y.; Anderson, N.; Ludwig, A.; Tinker, A. ATP-Sensitive Potassium Channels in the Sinoatrial Node Contribute to Heart Rate Control and Adaptation to Hypoxia. J. Biol. Chem. 2018, 293, 8912–8921. [Google Scholar] [CrossRef]
  10. Bogusławski, W.; Solarz-Bogusławska, J. Heart rate variability. Med. Rodz. 2017, 4, 265–272. [Google Scholar]
  11. Sun, Z.-Q.; Ojamaa, K.; Nakamura, T.Y.; Artman, M.; Klein, I.; Coetzee, W.A. Thyroid Hormone Increases Pacemaker Activity in Rat Neonatal Atrial Myocytes. J. Mol. Cell. Cardiol. 2001, 33, 811–824. [Google Scholar] [CrossRef] [PubMed]
  12. von Olshausen, K.; Bischoff, S.; Kahaly, G.; Mohr-Kahaly, S.; Erbel, R.; Beyer, J.; Meyer, J. Cardiac Arrhythmias and Heart Rate in Hyperthyroidism. Am. J. Cardiol. 1989, 63, 930–933. [Google Scholar] [CrossRef]
  13. Dai, R.; Dheen, T.S.; Tay, S. Induction of Cytokine Expression in Rat Post-Ischemic Sinoatrial Node (SAN). Cell Tissue Res. 2002, 310, 59–66. [Google Scholar] [CrossRef]
  14. Finley, J.P.; Nugent, S.T. Heart Rate Variability in Infants, Children and Young Adults. J. Auton. Nerv. Syst. 1995, 51, 103–108. [Google Scholar] [CrossRef] [PubMed]
  15. Shaffer, F.; Ginsberg, J.P. An Overview of Heart Rate Variability Metrics and Norms. Front. Public Health 2017, 5, 258. [Google Scholar] [CrossRef] [PubMed]
  16. Kamen, P.W.; Krum, H.; Tonkin, A.M. Poincaré Plot of Heart Rate Variability Allows Quantitative Display of Parasympathetic Nervous Activity in Humans. Clin. Sci. 1996, 91, 201–208. [Google Scholar] [CrossRef] [PubMed]
  17. Ardissino, M.; Nicolaou, N.; Vizcaychipi, M. Noninvasive Real-Time Autonomic Function Characterization during Surgery via Continuous Poincaré Quantification of Heart Rate Variability. J. Clin Monit. Comput. 2019, 33, 627–635. [Google Scholar] [CrossRef]
  18. Guzik, P.; Piskorski, J.; Krauze, T.; Wykretowicz, A.; Wysocki, H. Heart Rate Asymmetry by Poincaré Plots of RR Intervals. Biomed. Tech. 2006, 51, 272–275. [Google Scholar] [CrossRef]
  19. Piskorski, J.; Guzik, P. Geometry of the Poincaré Plot of RR Intervals and Its Asymmetry in Healthy Adults. Physiol. Meas. 2007, 28, 287–300. [Google Scholar] [CrossRef]
  20. Brennan, M.; Palaniswami, M.; Kamen, P. Do Existing Measures of Poincare Plot Geometry Reflect Nonlinear Features of Heart Rate Variability? IEEE. Trans. Biomed. Eng. 2001, 48, 1342–1347. [Google Scholar] [CrossRef] [PubMed]
  21. Electrophysiology Task Force of the European Society of Cardiology the North American Society of Pacing Heart Rate Variability. Circulation 1996, 93, 1043–1065. [CrossRef]
  22. Parati, G.; Saul, J.P.; Di Rienzo, M.; Mancia, G. Spectral Analysis of Blood Pressure and Heart Rate Variability in Evaluating Cardiovascular Regulation: A Critical Appraisal. Hypertension 1995, 25, 1276–1286. [Google Scholar] [CrossRef]
  23. Eckberg, D.L. Sympathovagal Balance: A Critical Appraisal. Circulation 1997, 96, 3224–3232. [Google Scholar] [CrossRef]
  24. Marmerstein, J.T.; McCallum, G.A.; Durand, D.M. Direct Measurement of Vagal Tone in Rats Does Not Show Correlation to HRV. Sci. Rep. 2021, 11, 1210. [Google Scholar] [CrossRef]
  25. Billman, G.E. Heart Rate Variability—A Historical Perspective. Front. Physiol. 2011, 2, 86. [Google Scholar] [CrossRef] [PubMed]
  26. Tsuji, H.; Larson, M.G.; Venditti, F.J.; Manders, E.S.; Evans, J.C.; Feldman, C.L.; Levy, D. Impact of Reduced Heart Rate Variability on Risk for Cardiac Events. The Framingham Heart Study. Circulation 1996, 94, 2850–2855. [Google Scholar] [CrossRef] [PubMed]
  27. Rennie, K.; Hemingway, H.; Kumari, M.; Brunner, E.; Malik, M.; Marmot, M. Effects of Moderate and Vigorous Physical Activity on Heart Rate Variability in a British Study of Civil Servants. Am. J. Epidemiol. 2003, 158, 135–143. [Google Scholar] [CrossRef] [PubMed]
  28. Frank, J.; Seifert, G.; Schroeder, R.; Gruhn, B.; Stritter, W.; Jeitler, M.; Steckhan, N.; Kessler, C.S.; Michalsen, A.; Voss, A. Yoga in School Sports Improves Functioning of Autonomic Nervous System in Young Adults: A Non-Randomized Controlled Pilot Study. PLoS ONE 2020, 15, e0231299. [Google Scholar] [CrossRef]
  29. Wang, X.; Yan, C.; Shi, B.; Liu, C.; Karmakar, C.; Li, P. Does the Temporal Asymmetry of Short-Term Heart Rate Variability Change during Regular Walking? A Pilot Study of Healthy Young Subjects. Comput. Math. Methods. Med. 2018, 2018, 3543048. [Google Scholar] [CrossRef]
  30. Porta, A.; Casali, K.R.; Casali, A.G.; Gnecchi-Ruscone, T.; Tobaldini, E.; Montano, N.; Lange, S.; Geue, D.; Cysarz, D.; Van Leeuwen, P. Temporal Asymmetries of Short-Term Heart Period Variability Are Linked to Autonomic Regulation. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2008, 295, R550–R557. [Google Scholar] [CrossRef] [PubMed]
  31. Chladekova, L.; Czippelova, B.; Turianikova, Z.; Tonhajzerova, I.; Calkovska, A.; Baumert, M.; Javorka, M. Multiscale Time Irreversibility of Heart Rate and Blood Pressure Variability during Orthostasis. Physiol. Meas. 2012, 33, 1747–1756. [Google Scholar] [CrossRef] [PubMed]
  32. Visnovcova, Z.; Mestanik, M.; Javorka, M.; Mokra, D.; Gala, M.; Jurko, A.; Calkovska, A.; Tonhajzerova, I. Complexity and Time Asymmetry of Heart Rate Variability Are Altered in Acute Mental Stress. Physiol. Meas. 2014, 35, 1319. [Google Scholar] [CrossRef]
  33. Klintworth, A.; Ajtay, Z.; Paljunite, A.; Szabados, S.; Hejjel, L. Heart Rate Asymmetry Follows the Inspiration/Expiration Ratio in Healthy Volunteers. Physiol. Meas. 2012, 33, 1717–1731. [Google Scholar] [CrossRef] [PubMed]
  34. Massin, M.; von Bernuth, G. Clinical and Haemodynamic Correlates of Heart Rate Variability in Children with Congenital Heart Disease. Eur. J. Pediatr. 1998, 157, 967–971. [Google Scholar] [CrossRef] [PubMed]
  35. Kardelen, F.; Akçurin, G.; Ertuğ, H.; Akcurin, S.; Bircan, I. Heart Rate Variability and Circadian Variations in Type 1 Diabetes Mellitus. Pediatr. Diabetes 2006, 7, 45–50. [Google Scholar] [CrossRef] [PubMed]
  36. Akinci, A.; Celiker, A.; Baykal, E.; Teziç, T. Heart Rate Variability in Diabetic Children: Sensitivity of the Time- and Frequency-Domain Methods. Pediatr. Cardiol. 1993, 14, 140–146. [Google Scholar] [CrossRef]
  37. de Carvalho, T.D.; Wajnsztejn, R.; de Abreu, L.C.; Marques Vanderlei, L.C.; Godoy, M.F.; Adami, F.; Valenti, V.E.; Monteiro, C.B.M.; Leone, C.; da Cruz Martins, K.C.; et al. Analysis of Cardiac Autonomic Modulation of Children with Attention Deficit Hyperactivity Disorder. Neuropsychiatr. Dis. Treat. 2014, 10, 613–618. [Google Scholar] [CrossRef]
  38. Dahlqvist, J.A.; Karlsson, M.; Wiklund, U.; Hörnsten, R.; Strömvall-Larsson, E.; Berggren, H.; Hanseus, K.; Johansson, S.; Rydberg, A. Heart Rate Variability in Children with Fontan Circulation: Lateral Tunnel and Extracardiac Conduit. Pediatr. Cardiol. 2012, 33, 307–315. [Google Scholar] [CrossRef]
  39. Bobkowski, W.; Stefaniak, M.E.; Krauze, T.; Gendera, K.; Wykretowicz, A.; Piskorski, J.; Guzik, P. Measures of Heart Rate Variability in 24-h ECGs Depend on Age but Not Gender of Healthy Children. Front. Physiol. 2017, 8, 311. [Google Scholar] [CrossRef]
  40. Rękawek, J.; Miszczak-Knecht, M.; Mielniczuk, W.K. i J. Zmienność Rytmu Serca u Zdrowych Dzieci. Folia Cardiol. 2003, 10, 203–211. [Google Scholar]
  41. Umetani, K.; Singer, D.H.; McCraty, R.; Atkinson, M. Twenty-Four Hour Time Domain Heart Rate Variability and Heart Rate: Relations to Age and Gender over Nine Decades. J. Am. Coll. Cardiol. 1998, 31, 593–601. [Google Scholar] [CrossRef]
  42. Goto, M.; Nagashima, M.; Baba, R.; Nagano, Y.; Yokota, M.; Nishibata, K.; Tsuji, A. Analysis of Heart Rate Variability Demonstrates Effects of Development on Vagal Modulation of Heart Rate in Healthy Children. J. Pediatr. 1997, 130, 725–729. [Google Scholar] [CrossRef]
  43. Michels, N.; Clays, E.; De Buyzere, M.; Huybrechts, I.; Marild, S.; Vanaelst, B.; De Henauw, S.; Sioen, I. Determinants and Reference Values of Short-Term Heart Rate Variability in Children. Eur. J. Appl. Physiol. 2013, 113, 1477–1488. [Google Scholar] [CrossRef]
  44. Fukuba, Y.; Sato, H.; Sakiyama, T.; Yamaoka Endo, M.; Yamada, M.; Ueoka, H.; Miura, A.; Koga, S. Autonomic Nervous Activities Assessed by Heart Rate Variability in Pre- and Post-Adolescent Japanese. J. Physiol. Anthropol. 2009, 28, 269–273. [Google Scholar] [CrossRef] [Green Version]
  45. Galeev, A.R.; Igisheva, L.N.; Kazin, E.M. [Heart rate variability in healthy six- to sixteen year old children]. Fiziol. Cheloveka. 2002, 28, 54–58. [Google Scholar]
  46. Gąsior, J.S.; Sacha, J.; Jeleń, P.J.; Pawłowski, M.; Werner, B.; Dąbrowski, M.J. Interaction Between Heart Rate Variability and Heart Rate in Pediatric Population. Front. Physiol. 2015, 6, 385. [Google Scholar] [CrossRef]
  47. Cysarz, D.; Linhard, M.; Edelhäuser, F.; Längler, A.; Van Leeuwen, P.; Henze, G.; Seifert, G. Unexpected Course of Nonlinear Cardiac Interbeat Interval Dynamics during Childhood and Adolescence. PLoS ONE 2011, 6, e19400. [Google Scholar] [CrossRef]
  48. Harteveld, L.M.; Nederend, I.; ten Harkel, A.D.J.; Schutte, N.M.; de Rooij, S.R.; Vrijkotte, T.G.M.; Oldenhof, H.; Popma, A.; Jansen, L.M.C.; Suurland, J.; et al. Maturation of the Cardiac Autonomic Nervous System Activity in Children and Adolescents. J. Am. Heart Assoc. 2021, 10, e017405. [Google Scholar] [CrossRef]
  49. Clairambault, J.; Curzi-Dascalova, L.; Kauffmann, F.; Médigue, C.; Leffler, C. Heart Rate Variability in Normal Sleeping Full-Term and Preterm Neonates. Early Hum. Dev. 1992, 28, 169–183. [Google Scholar] [CrossRef]
  50. Lenard, Z.; Studinger, P.; Mersich, B.; Kocsis, L.; Kollai, M. Maturation of Cardiovagal Autonomic Function from Childhood to Young Adult Age. Circulation 2004, 110, 2307–2312. [Google Scholar] [CrossRef]
  51. Silvetti, M.S.; Drago, F.; Ragonese, P. Heart Rate Variability in Healthy Children and Adolescents Is Partially Related to Age and Gender. Int. J. Cardiol. 2001, 81, 169–174. [Google Scholar] [CrossRef]
  52. Nunan, D.; Sandercock, G.R.H.; Brodie, D.A. A Quantitative Systematic Review of Normal Values for Short-Term Heart Rate Variability in Healthy Adults. Pacing. Clin. Electrophysiol. 2010, 33, 1407–1417. [Google Scholar] [CrossRef]
  53. Bonnemeier, H.; Richardt, G.; Potratz, J.; Wiegand, U.K.H.; Brandes, A.; Kluge, N.; Katus, H.A. Circadian Profile of Cardiac Autonomic Nervous Modulation in Healthy Subjects: Differing Effects of Aging and Gender on Heart Rate Variability. J. Cardiovasc. Electrophysiol. 2003, 14, 791–799. [Google Scholar] [CrossRef]
  54. Almeida-Santos, M.A.; Barreto-Filho, J.A.; Oliveira, J.L.M.; Reis, F.P.; da Cunha Oliveira, C.C.; Sousa, A.C.S. Aging, Heart Rate Variability and Patterns of Autonomic Regulation of the Heart. Arch. Gerontol. Geriatr. 2016, 63, 1–8. [Google Scholar] [CrossRef]
  55. Koenig, J.; Thayer, J.F. Sex Differences in Healthy Human Heart Rate Variability: A Meta-Analysis. Neurosci. Biobehav. Rev. 2016, 64, 288–310. [Google Scholar] [CrossRef] [PubMed]
  56. Young, F.L.S.; Leicht, A.S. Short-Term Stability of Resting Heart Rate Variability: Influence of Position and Gender. Appl. Physiol. Nutr. Metab. 2011, 36, 210–218. [Google Scholar] [CrossRef] [PubMed]
  57. Seppälä, S.; Laitinen, T.; Tarvainen, M.P.; Tompuri, T.; Veijalainen, A.; Savonen, K.; Lakka, T. Normal Values for Heart Rate Variability Parameters in Children 6-8 Years of Age: The PANIC Study. Clin. Physiol. Funct. Imaging 2014, 34, 290–296. [Google Scholar] [CrossRef] [PubMed]
  58. Sharma, V.K.; Subramanian, S.K.; Arunachalam, V.; Rajendran, R. Heart Rate Variability in Adolescents-Normative Data Stratified by Sex and Physical Activity. J. Clin. Diagn. Res. 2015, 9, CC08–CC13. [Google Scholar] [CrossRef] [PubMed]
  59. Jarrin, D.C.; McGrath, J.J.; Poirier, P.; Séguin, L.; Tremblay, R.E.; Montplaisir, J.Y.; Paradis, G.; Séguin, J.R. Short-Term Heart Rate Variability in a Population-Based Sample of 10-Year-Old Children. Pediatr. Cardiol. 2015, 36, 41–48. [Google Scholar] [CrossRef]
  60. Piskorski, J.; Guzik, P. Asymmetric Properties of Long-Term and Total Heart Rate Variability. Med. Biol. Eng. Comput. 2011, 49, 1289–1297. [Google Scholar] [CrossRef]
  61. Piskorski, J.; Guzik, P. Compensatory Properties of Heart Rate Asymmetry. J. Electrocardiol. 2012, 45, 220–224. [Google Scholar] [CrossRef]
  62. Karmakar, C.K.; Khandoker, A.H.; Gubbi, J.; Palaniswami, M. Defining Asymmetry in Heart Rate Variability Signals Using a Poincaré Plot. Physiol. Meas. 2009, 30, 1227–1240. [Google Scholar] [CrossRef]
  63. Piskorski, J.; Ellert, J.; Krauze, T.; Grabowski, W.; Wykretowicz, A.; Guzik, P. Testing Heart Rate Asymmetry in Long, Nonstationary 24 Hour RR-Interval Time Series. Physiol. Meas. 2019, 40, 105001. [Google Scholar] [CrossRef]
  64. Piskorski, J.; Guzik, P. The Structure of Heart Rate Asymmetry: Deceleration and Acceleration Runs. Physiol. Meas. 2011, 32, 1011–1023. [Google Scholar] [CrossRef]
  65. Guzik, P.; Piskorski, J.; Contreras, P.; Migliaro, E.R. Asymmetrical Properties of Heart Rate Variability in Type 1 Diabetes. Clin. Auton. Res. 2010, 20, 255–257. [Google Scholar] [CrossRef] [PubMed]
  66. Guzik, P.; Piskorski, J.; Krauze, T.; Wykretowicz, A.; Wysocki, H. Partitioning Total Heart Rate Variability. Int. J. Cardiol. 2010, 144, 138–139. [Google Scholar] [CrossRef] [PubMed]
  67. Guzik, P.; Piskorski, J.; Krauze, T.; Narkiewicz, K.; Wykretowicz, A.; Wysocki, H. Asymmetric Features of Short-Term Blood Pressure Variability. Hypertens. Res. 2010, 33, 1199–1205. [Google Scholar] [CrossRef] [PubMed]
  68. Guzik, P.; Zuchowski, B.; Blaszyk, K.; Seniuk, W.; Wasniewski, M.; Gwizdala, A.; Wykretowicz, A.; Piskorski, J. Asymmetry of the Variability of Heart Rate and Conduction Time Between Atria and Ventricles. Circulation 2013, 77, 2904–2911. [Google Scholar] [CrossRef] [PubMed]
  69. Guzik, P.; Piskorski, J.; Ellert, J.; Krauze, T. Asymmetry of Haemodynamic Variability in Healthy People. In Proceedings of the 2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), Trento, Italy, 25–28 May 2014; IEEE: Trento, Italy, 2014; pp. 129–130. [Google Scholar]
  70. Bauer, A.; Malik, M.; Schmidt, G.; Barthel, P.; Bonnemeier, H.; Cygankiewicz, I.; Guzik, P.; Lombardi, F.; Müller, A.; Oto, A.; et al. Heart Rate Turbulence: Standards of Measurement, Physiological Interpretation, and Clinical Use. J. Am. Coll. Cardiol. 2008, 52, 1353–1365. [Google Scholar] [CrossRef] [PubMed]
  71. Guzik, P.; Schmidt, G. Clinical Significance of Heart Rate Turbulence. Kardiol. Pol. 2006, 64, 198–207. [Google Scholar]
  72. Grimm, W.; Sharkova, J.; Christ, M.; Schneider, R.; Schmidt, G.; Maisch, B. Heart Rate Turbulence Following Ventricular Premature Beats in Healthy Controls. Ann. Noninvasive Electrocardiol. 2003, 8, 127–131. [Google Scholar] [CrossRef]
  73. Kowalewski, M.; Alifier, M.; Bochen, D.; Urban, M. Heart Rate Turbulence in Children—Age and Heart Rate Relationships. Pediatr. Res. 2007, 62, 710–714. [Google Scholar] [CrossRef]
  74. De Maria, B.; Bari, V.; Cairo, B.; Vaini, E.; Martins de Abreu, R.; Perseguini, N.M.; Milan-Mattos, J.; Rehder-Santos, P.; Minatel, V.; Catai, A.M.; et al. Cardiac Baroreflex Hysteresis Is One of the Determinants of the Heart Period Variability Asymmetry. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2019, 317, R539–R551. [Google Scholar] [CrossRef]
  75. Parlow, J.; Viale, J.-P.; Annat, G.; Hughson, R.; Quintin, L. Spontaneous Cardiac Baroreflex in Humans. Hypertension 1995, 25, 1058–1068. [Google Scholar] [CrossRef] [PubMed]
  76. Rudas, L.; Crossman, A.A.; Morillo, C.A.; Halliwill, J.R.; Tahvanainen, K.U.; Kuusela, T.A.; Eckberg, D.L. Human sympathetic and vagal baroreflex responses to sequential nitroprusside and phenylephrine. Am. J. Physiol. 1999, 276, H1691–H1698. [Google Scholar] [CrossRef] [PubMed]
  77. Kubiak, K.B.; Więckowska, B.; Krauze, T.; Piskorski, J.; Guzik, P. Asymmetrical properties of the spontaneous baroreceptor reflex. In Proceedings of the 2022 12th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), Vysoké Tatry, Štrbské Pleso, Slovakia, 9–12 October 2022; pp. 1–2. [Google Scholar] [CrossRef]
  78. Kubiak, K.B.; Więckowska, B.; Krauze, T.; Piskorski, J.; Guzik, P. Detection of the baroreflex function changes during the 30-minute supine rest by the Poincaré plot-based method. In Proceedings of the 2022 12th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), Vysoké Tatry, Štrbské Pleso, Slovakia, 9–12 October 2022; pp. 1–2. [Google Scholar] [CrossRef]
  79. Karmakar, C.; Khandoker, A.; Kimura, Y.; Palaniswami, M. Investigating Foetal Heart Rate Asymmetry. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; pp. 2261–2264. [Google Scholar] [CrossRef]
  80. López-Justo, C.; Pliego-Carrillo, A.C.; Ledesma-Ramírez, C.I.; Mendieta-Zerón, H.; Peña-Castillo, M.Á.; Echeverría, J.C.; Rodríguez-Arce, J.; Reyes-Lagos, J.J. Differences in the Asymmetry of Beat-to-Beat Fetal Heart Rate Accelerations and Decelerations at Preterm and Term Active Labor. Sensors 2021, 10, 8249. [Google Scholar] [CrossRef]
  81. Kramarić, K.; Šapina, M.; Garcin, M.; Milas, K.; Pirić, M.; Brdarić, D.; Lukić, G.; Milas, V.; Pušeljić, S. Heart rate asymmetry as a new marker for neonatal stress. Biomed. Signal Process. Control 2019, 47, 219–223. [Google Scholar] [CrossRef]
  82. Czippelova, B.; Chladekova, L.; Uhrikova, Z.; Javorka, K.; Zibolen, M.; Javorka, M. Time irreversibility of heart rate oscillations in newborns—Does it reflect system nonlinearity? Biomed. Signal Process. Control 2015, 19, 85–88. [Google Scholar] [CrossRef]
  83. Tonhajzerova, I.; Ondrejka, I.; Chladekova, L.; Farsky, I.; Visnovcova, Z.; Calkovska, A.; Jurko, A.; Javorka, M. Heart Rate Time Irreversibility Is Impaired in Adolescent Major Depression. Prog. Neuropsychopharmacol. Biol. Psychiatry 2012, 39, 212–217. [Google Scholar] [CrossRef]
  84. Tonhajzerová, I.; Ondrejka, I.; Farský, I.; Višňovcová, Z.; Mešťaník, M.; Javorka, M.; Jurko, A.; Čalkovská, A. Attention Deficit/Hyperactivity Disorder (ADHD) Is Associated with Altered Heart Rate Asymmetry. Physiol. Res. 2014, 63 (Suppl. S4), S509–S519. [Google Scholar] [CrossRef]
  85. Pawłowski, R.; Zalewski, P.; Newton, J.; Piątkowska, A.; Koźluk, E.; Opolski, G.; Buszko, K. An assessment of heart rate and blood pressure asymmetry in the diagnosis of vasovagal syncope in females. Fron. Physiol. 2023, 13, 2738. [Google Scholar] [CrossRef]
  86. Mina-Paz, Y.; Santana-García, V.N.; Tafur-Tascon, L.J.; Cabrera-Hernández, M.A.; Pliego-Carrillo, A.C.; Reyes-Lagos, J.J. Analysis of Short-Term Heart Rate Asymmetry in High-Performance Athletes and Non-Athletes. Symmetry 2022, 14, 1229. [Google Scholar] [CrossRef]
  87. Guzik, P.; Orłowska-Baranowska, E.; Piskorski, J.; Baranowski, R. Expression of Heart Rate Asymmetry Is Related to the NYHA Class of Heart Failure in Patients with Aortic Stenosis. Folia Cardiol. Excerpta 2008, 3, 40. [Google Scholar]
  88. Platiša, M.M.; Radovanović, N.N.; Kalauzi, A.; Milašinović, G.; Pavlović, S.U. Differentiation of Heart Failure Patients by the Ratio of the Scaling Exponents of Cardiac Interbeat Intervals. Front. Physiol. 2019, 10, 570. [Google Scholar] [CrossRef] [PubMed]
  89. Karmakar, C.K.; Jelinek, H.F.; Warner, P.; Khandoker, A.H.; Palaniswami, M. Effect of Gender and Diabetes on Major Depressive Disorder Using Heart Rate Asymmetry. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; pp. 6679–6682. [Google Scholar] [CrossRef]
  90. Shi, B.; Wang, L.; Yan, C.; Chen, D.; Liu, M.; Li, P. Nonlinear heart rate variability biomarkers for gastric cancer severity: A pilot study. Sci. Rep. 2019, 9, 13833. [Google Scholar] [CrossRef]
  91. Jelinek, H.F.; August, K.G.; Imam, M.H.; Khandoker, A.H.; Koenig, A.; Riener, R. Heart rate asymmetry and emotional response to robot-assist task challenges in post-stroke patients. Comput. Cardiol. 2011, 38, 521–524. [Google Scholar]
  92. Porta, A.; D’addio, G.; Bassani, T.; Maestri, R.; Pinna, G.D. Assessment of Cardiovascular Regulation through Irreversibility Analysis of Heart Period Variability: A 24 Hours Holter Study in Healthy and Chronic Heart Failure Populations. Philos. Trans. R. Soc. A 2009, 367, 1359–1375. [Google Scholar] [CrossRef]
  93. Pawłowski, R.; Buszko, K.; Newton, J.L.; Kujawski, S.; Zalewski, P. Heart Rate Asymmetry Analysis During Head-Up Tilt Test in Healthy Men. Front. Physiol. 2021, 12, 657902. [Google Scholar] [CrossRef]
  94. Karmakar, C.; Khandoker, A.; Palaniswami, M. Investigating the Changes in Heart Rate Asymmetry (HRA) with Perturbation of Parasympathetic Nervous System. Australas. Phys. Eng. Sci. Med. 2012, 35, 465–474. [Google Scholar] [CrossRef]
Jcm 12 01194 g001 550
Figure 1. An example of the Poincaré plot of RR intervals. For clarity, instead of RR intervals from the 24-h ECG recording, which has, on average, 80,000–120,000 RR intervals, we present a set of RR intervals from a 30-min ECG. The whole plot is built by points described by pairs of two consecutive RR intervals, i.e., the current RR interval (RRn) and the following next RR interval (RRn+1). The Poincaré plot of RR intervals in the space (RRn, RRn+1) has many interesting physiological properties. Points described by RR intervals of the same duration (RRn+1 = RRn) (black dots) create the diagonal line (dashed grey line) termed the identity line. This line separates HR decelerations (green dots, RRn+1 > RRn) from HR accelerations (red dots, RRn+1 < RRn). Several variance-based descriptors describe the short-term, long-term, and total HRA [60,61,62,63,66].
Figure 1. An example of the Poincaré plot of RR intervals. For clarity, instead of RR intervals from the 24-h ECG recording, which has, on average, 80,000–120,000 RR intervals, we present a set of RR intervals from a 30-min ECG. The whole plot is built by points described by pairs of two consecutive RR intervals, i.e., the current RR interval (RRn) and the following next RR interval (RRn+1). The Poincaré plot of RR intervals in the space (RRn, RRn+1) has many interesting physiological properties. Points described by RR intervals of the same duration (RRn+1 = RRn) (black dots) create the diagonal line (dashed grey line) termed the identity line. This line separates HR decelerations (green dots, RRn+1 > RRn) from HR accelerations (red dots, RRn+1 < RRn). Several variance-based descriptors describe the short-term, long-term, and total HRA [60,61,62,63,66].
Jcm 12 01194 g001
Table
Table 1. HRA parameters in the whole group (n = 96) and compared between girls (n = 50) and boys (n = 46) (Mann–Whitney test; * p < 0.05).
Table 1. HRA parameters in the whole group (n = 96) and compared between girls (n = 50) and boys (n = 46) (Mann–Whitney test; * p < 0.05).
ParameterAll ChildrenGirlsBoysp-Value
MedianIQRMedianIQRMedianIQR
SD1d (ms)34.2521.33–43.9732.0420.01–43.4036.1324.69–44.640.36
SD1a (ms)29.2119.80–37.8828.8319.13–37.4930.0921.19–40.080.50
SD2d (ms)153.07117.68–177.64153.59117.65–177.79148.72117.71–177.480.97
SD2a (ms)158.35126.79–191.71160.02127.44–193.25156.22124.79–191.500.82
SDNNd (ms)111.4988.63–129.36112.2790.15–128.58109.8986.43–129.720.96
SDNNa (ms)114.9691.77–137.54114.9892.71–137.61113.6191.32–137.470.83
C1d 0.560.53–0.580.550.51–0.570.570.53–0.580.03 *
C2d0.480.46–0.490.480.46–0.490.470.46–0.490.22
CTd0.480.47–0.490.480.47–0.490.470.46–0.490.24
Nd0.420.39–0.450.420.39–0.440.430.40–0.450.35
C1d—contribution of HR decelerations to the short-term HRV; C2d—contribution of HR decelerations to the long-term HRV; CTd—contribution of HR decelerations to the total HRV; IQR—interquartile range; Nd—contribution of the number of HR decelerations to the total number of RR intervals; SD1a—part of SD12 related to HR accelerations (square root); SD1d—part of SD12 related to HR decelerations (square root); SD2a—part of SD22 related to HR accelerations (square root); SD2d—part of SD22 related to HR decelerations (square root); SDNNa—part of SDNN2 related to HR accelerations (square root); SDNNd—part of SDNN2 related to HR decelerations (square root).
Table
Table 2. Prevalence of different types of HRA in the whole group (n = 96) and compared between girls (n = 50) and boys (n = 46) (Fisher’s exact test).
Table 2. Prevalence of different types of HRA in the whole group (n = 96) and compared between girls (n = 50) and boys (n = 46) (Fisher’s exact test).
Prevalence
All Children (%)p-ValueGirls (%)Boys (%)p-Value
HRA1 90 (93.7)<0.00145 (90.0)45 (97.8)0.12
HRA2 85 (88.5)<0.00144 (88.0)41 (89.1)0.86
HRAT 85 (88.5)<0.00144 (88.0)41 (89.1)0.86
HRAN 95 (99.0)<0.00149 (98.0)46 (89.1)0.34
HRAcomp85 (88.5)<0.00144 (88.0)41 (89.1)0.86
HRA1—short-term HRA; HRA2—long-term HRA; HRAcomp—HRA compensation; HRAN—HRA of the number of HR decelerations and accelerations; HRAT—total HRA.
Table
Table 3. The correlation between age and continuous HRA descriptors (n = 96) (Spearman’s rank correlation coefficient).
Table 3. The correlation between age and continuous HRA descriptors (n = 96) (Spearman’s rank correlation coefficient).
ParameterrhoSE of rhop-Value
SD1d −0.040.10310.7148
SD1a −0.050.10300.6592
SD2d 0.420.0938<0.0001
SD2a 0.340.09700.0007
SDNNd 0.360.09620.0003
SDNNa 0.310.09790.0018
C1d 0.010.100.9729
C2d0.050.100.5967
CTd0.040.100.7179
Nd0.030.10310.7353
C1d—contribution of HR decelerations to the short-term HRV; C2d—contribution of HR decelerations to the long-term HRV; CTd—contribution of HR decelerations to the total HRV; Nd—contribution of the number of HR decelerations to the total number of RR intervals; SD1a—part of SD12 related to HR accelerations (square root); SD1d—part of SD12 related to HR decelerations (square root); SD2a—part of SD22 related to HR accelerations (square root); SD2d—part of SD22 related to HR decelerations (square root); SDNNa—part of SDNN2 related to HR accelerations (square root); SDNNd—part of SDNN2 related to HR decelerations (square root); SE—standard error.
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Zalas, D.; Bobkowski, W.; Piskorski, J.; Guzik, P. Heart Rate Asymmetry in Healthy Children. J. Clin. Med. 2023, 12, 1194. https://doi.org/10.3390/jcm12031194

AMA Style

Zalas D, Bobkowski W, Piskorski J, Guzik P. Heart Rate Asymmetry in Healthy Children. Journal of Clinical Medicine. 2023; 12(3):1194. https://doi.org/10.3390/jcm12031194

Chicago/Turabian Style

Zalas, Dominika, Waldemar Bobkowski, Jarosław Piskorski, and Przemysław Guzik. 2023. "Heart Rate Asymmetry in Healthy Children" Journal of Clinical Medicine 12, no. 3: 1194. https://doi.org/10.3390/jcm12031194

APA Style

Zalas, D., Bobkowski, W., Piskorski, J., & Guzik, P. (2023). Heart Rate Asymmetry in Healthy Children. Journal of Clinical Medicine, 12(3), 1194. https://doi.org/10.3390/jcm12031194

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