Signatures of the autonomic nervous system and the heart's pacemaker cells in canine electrocardiograms and their applications to humans

Impact of QRS misclassifications on heart-rate-variability parameters (results from the CARLA cohort study)

BACKGROUND

Heart rate variability (HRV), an important marker of autonomic nervous system activity, is usually determined from electrocardiogram (ECG) recordings corrected for extrasystoles and artifacts. Especially in large population-based studies, computer-based algorithms are used to determine RR intervals. The Modular ECG Analysis System MEANS is a widely used tool, especially in large studies. The aim of this study was therefore to evaluate MEANS for its ability to detect non-sinus ECG beats and artifacts and to compare HRV parameters in relation to ECG processing. Additionally, we analyzed how ECG processing affects the statistical association of HRV with cardiovascular disease (CVD) risk factors.

METHODS

20-min ECGs from 1,674 subjects of the population-based CARLA study were available for HRV analysis. All ECGs were processed with the ECG computer program MEANS. A reference standard was established by experienced clinicians who visually inspected the MEANS-processed ECGs and reclassified beats if necessary. HRV parameters were calculated for 5-minute segments selected from the original 20-minute ECG. The effects of misclassified typified normal beats on i) HRV calculation and ii) the associations of CVD risk factors (sex, age, diabetes, myocardial infarction) with HRV were modeled using linear regression.

RESULTS

Compared to the reference standard, MEANS correctly classified 99% of all beats. The averaged sensitivity of MEANS across all ECGs to detect non-sinus beats was 76% [95% CI: 74.1;78.5], but for supraventricular extrasystoles detection sensitivity dropped to 38% [95% CI: 36.8;38.5]. Time-domain parameters were less affected by false sinus beats than frequency parameters. Compared to the reference standard, MEANS resulted in a higher SDNN on average (mean absolute difference 1.4ms [95% CI: 1.0;1.7], relative 4.9%). Other HRV parameters were also overestimated as well (between 6.5 and 29%). The effect estimates for the association of CVD risk factors with HRV did not differ between the editing methods.

CONCLUSION

We have shown that the use of the automated MEANS algorithm may lead to an overestimation of HRV due to the misclassification of non-sinus beats, especially in frequency domain parameters. However, in population-based studies, this has no effect on the observed associations of HRV with risk factors, and therefore an automated ECG analyzing algorithm as MEANS can be recommended here for the determination of HRV parameters.

Complexity synchronization in emergent intelligence

In this work, we use a simple multi-agent-based-model (MABM) of a social network, implementing selfish algorithm (SA) agents, to create an adaptive environment and show, using a modified diffusion entropy analysis (DEA), that the mutual-adaptive interaction between the parts of such a network manifests complexity synchronization (CS). CS has been shown to exist by processing simultaneously measured time series from among organ-networks (ONs) of the brain (neurophysiology), lungs (respiration), and heart (cardiovascular reactivity) and to be explained theoretically as a synchronization of the multifractal dimension (MFD) scaling parameters characterizing each time series. Herein, we find the same kind of CS in the emergent intelligence of groups formed in a self-organized social interaction without macroscopic control but with biased self-interest between two groups of agents playing an anti-coordination game. This computational result strongly suggests the existence of the same CS in real-world social phenomena and in human-machine interactions as that found empirically in ONs.

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.

Age-dependent contribution of intrinsic mechanisms to sinoatrial node function in humans

Average beat interval (BI) and beat interval variability (BIV) are primarily determined by mutual entrainment between the autonomic-nervous system (ANS) and intrinsic mechanisms that govern sinoatrial node (SAN) cell function. While basal heart rate is not affected by age in humans, age-dependent reductions in intrinsic heart rate have been documented even in so-called healthy individuals. The relative contributions of the ANS and intrinsic mechanisms to age-dependent deterioration of SAN function in humans are not clear. We recorded ECG on patients (n = 16 < 21 years and n = 23 41-78 years) in the basal state and after ANS blockade (propranolol and atropine) in the presence of propofol and dexmedetomidine anesthesia. Average BI and BIV were analyzed. A set of BIV features were tested to designated the "signatures" of the ANS and intrinsic mechanisms and also the anesthesia "signature". In young patients, the intrinsic mechanisms and ANS mainly contributed to long- and short-term BIV, respectively. In adults, both ANS and intrinsic mechanisms contributed to short-term BIV, while the latter also contributed to long-term BIV. Furthermore, anesthesia affected ANS function in young patients and both mechanisms in adult. The work also showed that intrinsic mechanism features can be calculated from BIs, without intervention.

Association and interaction between clinician-rated measures of depression and anxiety with heart rate variability in elderly patients with psychiatric disorders

Background

Older adults are vulnerable to comorbid depression and anxiety symptoms; however, these conditions are widely underrecognized and often untreated. Understanding their combined manifestation using objective measurements, such as clinician-rated scales and heart rate variability (HRV), can help refine the diagnosis and select a treatment strategy for geriatric patients.

Methods

This study included patients over 65 years who were mainly diagnosed with either category of depressive or anxiety disorders from the psychiatric outpatient clinic in a university hospital. A total of 114 patients met eligibility with a completed collection of electrocardiograms, the Hamilton Depression Rating Scale (HDRS; clinician-rated depression), and the Hamilton Anxiety Scale (HAS; clinician-rated anxiety) to assess the severity of symptoms. Both main and interaction effects between HDRS and HAS on HRV parameters were examined.

Results

Significant interaction effects between clinician-rated depression and anxiety (HDRS × HAS) on HRV reduction in frequency parameters (i.e., nuLF, nuHF, LF/HF ratio) were found, which consistently indicated autonomic nervous system dysregulation. Findings imply that HRV could reflect synergistic effects of comorbid depressive and anxiety symptoms, perhaps due to the amplification of individual symptoms in geriatric patients.

Conclusions

The results imply that using objective measurements can improve diagnostic accuracy, particularly in geriatric patients with comorbid status, and the normalization of the autonomic nervous system might be a candidate target for prevention and treatment.

Associations between physical activity and autonomic function during deep breathing test: the Swedish CArdioPulmonary bioImage Study (SCAPIS)

PURPOSE

The deep breathing test (DBT) is a sensitive test of cardiovagal function. The aim of this study was to explore associations between physical activity and sedentary time, measured by accelerometer, and autonomic function, using DBT.

METHODS

In the Swedish Cardio-Pulmonary bioImage Study, men and women aged 50-64 were randomly invited from the general population. A total of 4325 subjects who underwent DBT and assessment of physical activity and sedentary time by accelerometery were included. ECG files from 1-min DBT were used to calculate measures of respiratory sinus arrhythmia [RSA; expiration-inspiration (E-I) difference and E/I ratio], heart rate variability [HRV; root mean square of successive differences (RMSSD), standard deviation of heart rates and mean circular resultant]. Low RSA and HRV was defined as the lowest 10% in the population.

RESULTS

For accelerometer-assessed physical activity, there were significant associations between high percentage of sedentary time and low E/I (p < 0.01), and low RMSSD (p < 0.01) in an age- and sex-adjusted model, and between percentage of sedentary time and low RMSSD (p = 0.04) in a risk factor-adjusted model. Low RMSSD was less common in those with a high percentage of moderate to vigorous physical activity (p = 0.04, after risk-factor adjustment). These associations became non-significant when further adjusting for heart rate.

CONCLUSION

We report associations between degree of physical activity and indices of autonomic dysfunction in a large population. The relationships were no longer significant after adjustments for heart rate, indicating that the relationship between physical activity and cardiovagal function partly is accounted for by reduced heart rate.

Roles and mechanisms of natural drugs on sinus node dysfunction

Sinus node dysfunction is a common arrhythmia disorder with a high incidence and significant social and economic burden. Currently, there are no effective drugs for treating chronic sinus node dysfunction. The disease is associated with ion channel disturbances caused by aging, fibrosis, inflammation, oxidative stress, and autonomic dysfunction. Natural active substances and Chinese herbal medicines have been widely used and extensively studied in the medical community for the treatment of arrhythmias. Multiple studies have demonstrated that various active ingredients and Chinese herbal medicines, such as astragaloside IV, quercetin, and ginsenosides, exhibit antioxidant effects, reduce fibrosis, and maintain ion channel stability, providing promising drugs for treating sinus node dysfunction. This article summarizes the research progress on natural active ingredients and Chinese herbal formulas that regulate sick sinoatrial node function, providing valuable references for the treatment of sinus node dysfunction.

Differences in Long-Term Heart Rate Variability between Subjects with and without Metabolic Syndrome: A Systematic Review and Meta-Analysis

BACKGROUND

Our aim was to determine the impact that metabolic syndrome (MS) produces in long-term heart rate variability (HRV), quantitatively synthesizing the results of published studies to characterize the cardiac autonomic dysfunction in MS.

METHODS

We searched electronic databases for original research works with long-term HRV recordings (24 h) that compared people with MS (MS+) versus healthy people as a control group (MS-). This systematic review and meta-analysis (MA) was performed according to PRISMA guidelines and registered at PROSPERO (CRD42022358975).

RESULTS

A total of 13 articles were included in the qualitative synthesis, and 7 of them met the required criteria to be included in the MA. SDNN (-0.33 [-0.57, 0.09], p = 0.008), LF (-0.32 [-0.41, -0.23], p < 0.00001), VLF (-0.21 [-0.31, -0.10], p = 0.0001) and TP (-0.20 [-0.33, -0.07], p = 0.002) decreased in patients with MS. The rMSSD (p = 0.41), HF (p = 0.06) and LF/HF ratio (p = 0.64) were not modified.

CONCLUSIONS

In long-term recordings (24 h), SDNN, LF, VLF and TP were consistently decreased in patients with MS. Other parameters that could be included in the quantitative analysis were not modified in MS+ patients (rMSSD, HF, ratio LF/HF). Regarding non-linear analyses, the results are not conclusive due to the low number of datasets found, which prevented us from conducting an MA.

Emergent activity, heterogeneity, and robustness in a calcium feedback model of the sinoatrial node

The sinoatrial node (SAN) is the primary pacemaker of the heart. SAN activity emerges at an early point in life and maintains a steady rhythm for the lifetime of the organism. The ion channel composition and currents of SAN cells can be influenced by a variety of factors. Therefore, the emergent activity and long-term stability imply some form of dynamical feedback control of SAN activity. We adapt a recent feedback model-previously utilized to describe control of ion conductances in neurons-to a model of SAN cells and tissue. The model describes a minimal regulatory mechanism of ion channel conductances via feedback between intracellular calcium and an intrinsic target calcium level. By coupling a SAN cell to the calcium feedback model, we show that spontaneous electrical activity emerges from quiescence and is maintained at steady state. In a 2D SAN tissue model, spatial variability in intracellular calcium targets lead to significant, self-organized heterogeneous ion channel expression and calcium transients throughout the tissue. Furthermore, multiple pacemaking regions appear, which interact and lead to time-varying cycle length, demonstrating that variability in heart rate is an emergent property of the feedback model. Finally, we demonstrate that the SAN tissue is robust to the silencing of leading cells or ion channel knockouts. Thus, the calcium feedback model can reproduce and explain many fundamental emergent properties of activity in the SAN that have been observed experimentally based on a minimal description of intracellular calcium and ion channel regulatory networks.

A predictive model of response to metoprolol in children and adolescents with postural tachycardia syndrome

BACKGROUND

The present work was designed to explore whether electrocardiogram (ECG) index-based models could predict the effectiveness of metoprolol therapy in pediatric patients with postural tachycardia syndrome (POTS).

METHODS

This study consisted of a training set and an external validation set. Children and adolescents with POTS who were given metoprolol treatment were enrolled, and after follow-up, they were grouped into non-responders and responders depending on the efficacy of metoprolol. The difference in pre-treatment baseline ECG indicators was analyzed between the two groups in the training set. Binary logistic regression analysis was further conducted on the association between significantly different baseline variables and therapeutic efficacy. Nomogram models were established to predict therapeutic response to metoprolol. The receiver-operating characteristic curve (ROC), calibration, and internal validation were used to evaluate the prediction model. The predictive ability of the model was validated in the external validation set.

RESULTS

Of the 95 enrolled patients, 65 responded to metoprolol treatment, and 30 failed to respond. In the responders, the maximum value of the P wave after correction (Pcmax), P wave dispersion (Pd), Pd after correction (Pcd), QT interval dispersion (QTd), QTd after correction (QTcd), maximum T-peak-to-T-end interval (Tpemax), and T-peak-to-T-end interval dispersion (Tped) were prolonged (all P < 0.01), and the P wave amplitude was increased (P < 0.05) compared with those of the non-responders. In contrast, the minimum value of the P wave duration after correction (Pcmin), the minimum value of the QT interval after correction (QTcmin), and the minimum T-peak-to-T-end interval (Tpemin) in the responders were shorter (P < 0.01, < 0.01 and < 0.01, respectively) than those in the non-responders. The above indicators were screened based on the clinical significance and multicollinearity analysis to construct a binary logistic regression. As a result, pre-treatment Pcmax, QTcmin, and Tped were identified as significantly associated factors that could be combined to provide an accurate prediction of the therapeutic response to metoprolol among the study subjects, yielding good discrimination [area under curve (AUC) = 0.970, 95% confidence interval (CI) 0.942-0.998] with a predictive sensitivity of 93.8%, specificity of 90.0%, good calibration, and corrected C-index of 0.961. In addition, the calibration curve and standard curve had a good fit. The accuracy of internal validation with bootstrap repeated sampling was 0.902. In contrast, the kappa value was 0.769, indicating satisfactory agreement between the predictive model and the results from the actual observations. In the external validation set, the AUC for the prediction model was 0.895, and the sensitivity and specificity were 90.9% and 95.0%, respectively.

CONCLUSIONS

A high-precision predictive model was successfully developed and externally validated. It had an excellent predictive value of the therapeutic effect of metoprolol on POTS among children and adolescents.

Early transient dysautonomia predicts the risk of infantile epileptic spasm syndrome onset: A prospective cohort study

Background

Infantile epileptic spasm syndrome (IESS) is an age-dependent epileptic encephalopathy with a significant risk of developmental regression. This study investigates the association between heart rate variability (HRV) in infants at risk of IESS and the clinical onset of IESS.

Methods

Sixty neonates at risk of IESS were prospectively followed from birth to 12 months with simultaneous electroencephalogram (EEG) and electrocardiogram recordings for 60 min at every 2-month interval. HRV metrics were calculated from 5 min time-epoch during sleep including frequency domain measures, Poincare analysis including cardiac vagal index (CVI) and cardiac sympathetic index (CSI), and detrended fluctuation analysis (DFA α1, DFA α2). To assess the effect of each HRV metric at the 2-month baseline on the time until the first occurrence of either hypsarrhythmia on EEG and/or clinical spasm, univariate cox-proportional hazard models were fitted for each HRV metric.

Results

Infantile epileptic spasm syndrome was diagnosed in 20/60 (33%) of the cohort in a 12-month follow-up and 3 (5%) were lost to follow-up. The median age of developing hypsarrhythmia was 25 (7-53) weeks and clinical spasms at 24 (8-40) weeks. Three (5%) patients had clinical spasms without hypsarrhythmia, and 5 (8%) patients had hypsarrhythmia before clinical spasms at the initial presentation. The infants with high CSI (hazard ratio 2.5, 95% CI 1.2-5.2, P = 0.01) and high DFA α1 (hazard ratio 16, 95% CI 1.1-240, P = 0.04) at 2 months were more likely to develop hypsarrhythmia by the first year of age. There was a trend toward decreasing CSI and DFA α1 and increasing CVI in the first 8 months of age.

Conclusion

Our data suggest that relative sympathetic predominance at an early age of 2 months may be a potential predictor for developing IESS. Hence, early HRV patterns may provide valuable prognostic information in children at risk of IESS allowing early detection and optimization of cognitive outcomes. Whether early intervention to restore sympathovagal balance per se would provide clinical benefit must be addressed by future studies.

Emergence of heartbeat frailty in advanced age I: perspectives from life-long EKG recordings in adult mice

The combined influences of sinoatrial nodal (SAN) pacemaker cell automaticity and its response to autonomic input determine the heart's beating interval variability and mean beating rate. To determine the intrinsic SAN and autonomic signatures buried within EKG RR interval time series change in advanced age, we measured RR interval variability before and during double autonomic blockade at 3-month intervals from 6 months of age until the end of life in long-lived (those that achieved the total cohort median life span of 24 months and beyond) C57/BL6 mice. Prior to 21 months of age, time-dependent changes in intrinsic RR interval variability and mean RR interval were relatively minor. Between 21 and 30 months of age, however, marked changes emerged in intrinsic SAN RR interval variability signatures, pointing to a reduction in the kinetics of pacemaker clock mechanisms, leading to reduced synchronization of molecular functions within and among SAN cells. This loss of high-frequency signal processing within intrinsic SAN signatures resulted in a marked increase in the mean intrinsic RR interval. The impact of autonomic signatures on RR interval variability were net sympathetic and partially compensated for the reduced kinetics of the intrinsic SAN RR interval variability signatures, and partially, but not completely, shifted the EKG RR time series intervals to a more youthful pattern. Cross-sectional analyses of other subsets of C57/BL6 ages indicated that at or beyond the median life span of our longitudinal cohort, noncardiac, constitutional, whole-body frailty was increased, energetic efficiency was reduced, and the respiratory exchange ratio increased. We interpret the progressive reduction in kinetics in intrinsic SAN RR interval variability signatures in this context of whole-body frailty beyond 21 months of age to be a manifestation of "heartbeat frailty."

Evaluation of non-linear heart rate variability using multi-scale multi-fractal detrended fluctuation analysis in mice: Roles of the autonomic nervous system and sinoatrial node

Nonlinear analyses of heart rate variability (HRV) can be used to quantify the unpredictability, fractal properties and complexity of heart rate. Fractality and its analysis provides valuable information about cardiovascular health. Multi-Scale Multi-Fractal Detrended Fluctuation Analysis (MSMFDFA) is a complexity-based algorithm that can be used to quantify the multi-fractal dynamics of the HRV time series through investigating characteristic exponents at different time scales. This method is applicable to short time series and it is robust to noise and nonstationarity. We have used MSMFDFA, which enables assessment of HRV in the frequency ranges encompassing the very-low frequency and ultra-low frequency bands, to jointly assess multi-scale and multi-fractal dynamics of HRV signals obtained from telemetric ECG recordings in wildtype mice at baseline and after autonomic nervous system (ANS) blockade, from electrograms recorded from isolated atrial preparations and from spontaneous action potential recordings in isolated sinoatrial node myocytes. Data demonstrate that the fractal profile of the intrinsic heart rate is significantly different from the baseline heart rate in vivo, and it is also altered after ANS blockade at specific scales and fractal order domains. For beating rate in isolated atrial preparations and intrinsic heart rate in vivo, the average fractal structure of the HRV increased and multi-fractality strength decreased. These data demonstrate that fractal properties of the HRV depend on both ANS activity and intrinsic sinoatrial node function and that assessing multi-fractality at different time scales is an effective approach for HRV assessment.

Cardiovagal Function Measured by the Deep Breathing Test: Relationships With Coronary Atherosclerosis

Background

The cardiovagal function can be assessed by quantification of respiratory sinus arrhythmia (RSA) during a deep breathing test. However, population studies of RSA and coronary atherosclerosis are lacking. This population‐based study examined the relationship between RSA during deep breathing and coronary atherosclerosis, assessed by coronary artery calcium score (CACS).

Methods and Results

SCAPIS (Swedish Cardiopulmonary Bioimage Study) randomly invited men and women aged 50 to 64 years from the general population. CACS was obtained from computed tomography scanning, and deep breathing tests were performed in 4654 individuals. Expiration–inspiration differences (E‐Is) of heart rates were calculated, and reduced RSA was defined as E‐I in the lowest decile of the population. The relationship between reduced RSA and CACS (CACS≥100 or CACS≥300) was calculated using multivariable‐adjusted logistic regression. The proportion of CACS≥100 was 24% in the lowest decile of E‐I and 12% in individuals with E‐I above the lowest decile (P<0.001), and the proportion of CACS≥300 was 12% and 4.8%, respectively (P<0.001). The adjusted odds ratio (OR) for CACS≥100 was 1.42 (95% CI, 1.10–1.84) and the adjusted OR for CACS≥300 was 1.62 (95% CI, 1.15–2.28), when comparing the lowest E‐I decile with deciles 2 to 10. Adjusted ORs per 1 SD lower E‐I were 1.17 (P=0.001) for CACS≥100 and 1.28 (P=0.001) for CACS≥300.

Conclusions

Low RSA during deep breathing is associated with increased coronary atherosclerosis as assessed by CACS, independently of traditional cardiovascular risk factors. Cardiovagal dysfunction could be a prevalent and modifiable risk factor for coronary atherosclerosis in the general population.

Structural and Electrical Remodeling of the Sinoatrial Node in Diabetes: New Dimensions and Perspectives

The sinoatrial node (SAN) is composed of highly specialized cells that mandate the spontaneous beating of the heart through self-generation of an action potential (AP). Despite this automaticity, the SAN is under the modulation of the autonomic nervous system (ANS). In diabetes mellitus (DM), heart rate variability (HRV) manifests as a hallmark of diabetic cardiomyopathy. This is paralleled by an impaired regulation of the ANS, and by a pathological remodeling of the pacemaker structure and function. The direct effect of diabetes on the molecular signatures underscoring this pathology remains ill-defined. The recent focus on the electrical currents of the SAN in diabetes revealed a repressed firing rate of the AP and an elongation of its tracing, along with conduction abnormalities and contractile failure. These changes are blamed on the decreased expression of ion transporters and cell-cell communication ports at the SAN (i.e., HCN4, calcium and potassium channels, connexins 40, 45, and 46) which further promotes arrhythmias. Molecular analysis crystallized the RGS4 (regulator of potassium currents), mitochondrial thioredoxin-2 (reactive oxygen species; ROS scavenger), and the calcium-dependent calmodulin kinase II (CaMKII) as metabolic culprits of relaying the pathological remodeling of the SAN cells (SANCs) structure and function. A special attention is given to the oxidation of CaMKII and the generation of ROS that induce cell damage and apoptosis of diabetic SANCs. Consequently, the diabetic SAN contains a reduced number of cells with significant infiltration of fibrotic tissues that further delay the conduction of the AP between the SANCs. Failure of a genuine generation of AP and conduction of their derivative waves to the neighboring atrial myocardium may also occur as a result of the anti-diabetic regiment (both acute and/or chronic treatments). All together, these changes pose a challenge in the field of cardiology and call for further investigations to understand the etiology of the structural/functional remodeling of the SANCs in diabetes. Such an understanding may lead to more adequate therapies that can optimize glycemic control and improve health-related outcomes in patients with diabetes.

Effects of a tripartite intervention on biological stress in preterm infants during heel pricks for newborn screening: A randomized controlled trial

This prospective randomized trial examined the effects of a tripartite intervention (behavioral state modulation + nonnutritive sucking + tucking) on stress from procedural pain during heel pricks. Blood samples for routine screening were collected by heel pricks 48 h after birth (Stage 1) and at ≥37 weeks' gestation (Stage 2); salivary cortisol levels (SCLs) pre-prick (T0) and 20 min post-prick (T1) assessed stress. Preterm infants (n = 64) sampled by convenience at Level III neonatal care units were randomly assigned to the control condition (usual care) or intervention condition (tripartite intervention). Generalized estimating equations examined differences in salivary cortisol between conditions. After adjusting for effects of gestational age, postmenstrual age, and baseline SCLs, (1) at Stage 1, the change in salivary cortisol from T0 to T1 in preterm infants who received the tripartite intervention was, on average, significantly lower by 0.431 units (log scale) than the change in preterm infants who received the control condition (p < 0.001); (2) in the tripartite intervention condition, the difference between the change in mean SCLs from T0 to T1 at Stages 1 and 2 was significantly lower by 0.287 units (log scale), on average than between the change at Stages 1 and 2 in the control condition (p = 0.026). The provision of a tripartite intervention during heel prick significantly decreased the raise of SCLs compared with infants receiving usual care, suggesting lower stress. Clinicians could easily implement the tripartite intervention for heel-stick support; however, replication is needed before recommending its incorporation into routine heel stick and other stressful procedures.

Analysis of Heart Rate Variability in Normal and Diabetic ECG signals using Fragmentation Approach

In this work, an attempt is made to quantify the dynamics of the heart rate variability timeseries in normal and diabetic population using fragmentation metrics. ECG signals recorded during deep breathing and head tilt up experiments are utilized for this study. The QRS-wave of ECG is extracted using the Pan Tompkins Algorithm. Heart rate variability features such as heart rate, Percentage of Inflection Points (PIP) and Inverse of the Average Length of the acceleration/deceleration Segment (IALS) are extracted to quantify the variation in signal dynamics. The results indicate that the ECG signals and heart rate variability signals obtained in deep breathing and tilt exhibit varied characteristics in both normal and diabetics. Further, in the diabetic condition the fragmentation measures exhibit a higher value in both deep breathing and tilt which indicates increased alternations in the signal. Most of the extracted fragmentation features are statistically significant (p<0.005) in differentiating normal and diabetic population. It appears that this method of analysis has potential towards the development of systems for the noninvasive assessment of diabetes.Clinical Relevance- This establishes a technique to quantify the variation in cardiovascular dynamics in normal and diabetic population.

The effect of age on the heart rate variability of healthy subjects

In this work we study the characteristics of heart rate variability (HRV) as a function of age and gender. Our analysis covers a wider age range than that studied so far. It includes results previously reported in the literature and reveals behaviours not reported before. We can establish basic scale relationships in different HRV measurements. The mean value of the RR intervals shows a power-law behaviour independent of gender. Magnitudes such as the standard deviation or pNN50 show abrupt changes at around the age of 12 years, and above that age they show gender dependence, which mainly affects short-time (or high frequency) scales. We present a unified analysis for the calculation of the non-linear α and β parameters. Both parameters depend on age; they increase in the extremes of life and reach a minimum at around one year of age. These gender-independent changes occur at low frequencies and in scale ranges that depend on age. The results obtained in this work are discussed in terms of the effects of basal metabolic rate, hormonal regulation, and neuronal activity on heart rate variability. This work finally discusses how these findings influence the interpretation of HRV measurements from records of different lengths.

Assessing stress variations in children during the strange situation procedure: comparison of three widely used respiratory sinus arrhythmia estimation methods

Objective.The respiratory sinus arrhythmia (RSA) is a well-known marker of vagal activity that can be exploited to measure stress changes. RSA is usually estimated from heart rate variability (HRV). This study aims to compare the RSA obtained with three widely adopted methods showing their strengths and potential pitfalls.Approach.The three methods are tested on 69 healthy preschoolers undergoing a stressful protocol, the strange situation procedure (SSP). We compare the RSA estimated by the Porges method, the univariate autoregressive (AR) spectral analysis of the HRV signal, and the bivariate AR spectral analysis of HRV and respirogram signals. We examine RSA differences detected across the SSP episodes and correlation between the estimates provided by each method.Main results.The Porges and the bivariate AR approaches both detected significant differences (i.e. stress variations) in the RSA measured across the SSP. However, the latter method showed higher sensitivity to stress changes induced by the procedure, with the mean RSA variation between baseline and first separation from the mother (the most stressful condition) being significantly different among methods: Porges, -17.5%; univariate AR, -18.3%; bivariate AR, -23.7%. Moreover, the performances of the Porges algorithm were found strictly dependent on the applied preprocessing.Significance.Our findings confirm the bivariate AR analysis of the HRV and respiratory signals as a robust stress assessment tool that does not require any population-specific preprocessing of the signals and warn about using RSA estimates that neglect breath information in more natural experiments, such as those involving children, in which respiratory frequency changes are extremely likely.

Interactions of productivity, content of separate amino acids, general protein in blood serum of chickens and tone of the autonomic nervous system

The regulation of metabolic processes and maintenance of a stable internal environment of the body is the role of the autonomic nervous system (ANS). It influences all systems and organs, thus along with the neuro-humoral system providing control over the metabolism of proteins, fats and carbohydrates. Amino acids, as structural units of any protein molecule, are essential for the metabolism of proteins, enzymes and hormones. Certain amino acids are involved in neutralizing free radicals and are a donor of metal groups, can act as an energy source in case of a deficiency of carbohydrates and fats, and be structural units of connective tissue. Exchange of these compounds and their regulation by ANS have not been studied sufficiently, therefore, it is necessary to carry out research in this direction. The studies were aimed at finding the presence of the regulatory effect of ANS on the content of individual amino acids in the blood serum of 60-day-old Cobb-500 chickens. Determination of the ANS tone in Cobb-500 chickens at the age of 30–35 days was determined by the method of variation pulsometry. Electrocardiographic examination was performed in a quiet room without the use of sedatives. At the age of 60 days, blood was obtained from the saphenous vein of the chickens’ shoulder. Blood was sampled from 10 am to 1 pm, after a short fasting (2:00). It was found that sympathicotonic chickens had a higher content of individual amino acids compared to normotonics and vagotonics. In vagotonic chickens, the content of glycine and serine in the blood serum significantly exceeded the rate of chickens with a balanced tone of ANS. Correlative relationships between amino acid content were highly significant in poultry with balanced ANS tone. Vagotonic chickens had the highest correlations between ANS tone, heart rate and amino acid content (trend). Correlative relationships were present between ANS and body weight of chickens of different groups, serum protein. The correlation between total serum protein and body weight of chickens was different depending on the tone of the ANS. Fisher’s test revealed a significant effect of balanced ANS tone on the content of valine, serine and glycine and the weight of chickens. The effect of ANS tone on weight in vagotonic birds was high.

Slowing down as we age: aging of the cardiac pacemaker's neural control

The cardiac pacemaker ignites and coordinates the contraction of the whole heart, uninterruptedly, throughout our entire life. Pacemaker rate is constantly tuned by the autonomous nervous system to maintain body homeostasis. Sympathetic and parasympathetic terminals act over the pacemaker cells as the accelerator and the brake pedals, increasing or reducing the firing rate of pacemaker cells to match physiological demands. Despite the remarkable reliability of this tissue, the pacemaker is not exempt from the detrimental effects of aging. Mammals experience a natural and continuous decrease in the pacemaker rate throughout the entire lifespan. Why the pacemaker rhythm slows with age is poorly understood. Neural control of the pacemaker is remodeled from birth to adulthood, with strong evidence of age-related dysfunction that leads to a downshift of the pacemaker. Such evidence includes remodeling of pacemaker tissue architecture, alterations in the innervation, changes in the sympathetic acceleration and the parasympathetic deceleration, and alterations in the responsiveness of pacemaker cells to adrenergic and cholinergic modulation. In this review, we revisit the main evidence on the neural control of the pacemaker at the tissue and cellular level and the effects of aging on shaping this neural control.

Opening the Schrödinger Box: Short- and Long-Range Mammalian Heart Rate Variability

Background

The interactions between the autonomic nervous system (ANS), intrinsic systems (e.g., endocrine), and internal pacemaker mechanisms govern short (milliseconds–seconds)- and long (seconds–minutes)-range heart rate variability (HRV). However, there is a debate regarding the identity of the mechanism underlying HRV on each time scale. We aim to design a general method that accurately differentiates between the relative contribution of the ANS and pacemaker mechanisms to HRV in various mammals, without the need for drug perturbations or organ isolation. Additionally, we aim to explore the universality of the relative contribution of the ANS and pacemaker system of different mammals.

Methods

This work explored short- and long-range HRVs using published ECG data from dogs, rabbits, and mice. To isolate the effects of ANS on HRV, ECG segments recorded before and after ANS-blockade were compared.

Results

Differentiation of the ANS from extrinsic and intrinsic pacemaker mechanisms was successfully achieved. In dogs, the internal pacemaker mechanisms were the main contributors to long-range and the ANS to short-range HRV. In rabbits and mice, the ANS and the internal pacemaker mechanisms affected both time scales, and anesthesia changed the relative contribution of the pacemaker mechanism to short- and long-range HRVs. In mice, the extrinsic mechanisms affected long-range HRV, while their effect was negligible in rabbits.

Conclusion

We offer a novel approach to determine the relative contributions of ANS and extrinsic and intrinsic pacemaker mechanisms to HRV and highlight the importance of selecting mammalian research models with HRV mechanisms representative of the target species of interest.

SDNN24 Estimation from Semi-Continuous HR Measures

The standard deviation of the interval between QRS complexes recorded over 24 h (SDNN24) is an important metric of cardiovascular health. Wrist-worn fitness wearable devices record heart beats 24/7 having a complete overview of users' heart status. Due to motion artefacts affecting QRS complexes recording, and the different nature of the heart rate sensor used on wearable devices compared to ECG, traditionally used to compute SDNN24, the estimation of this important Heart Rate Variability (HRV) metric has never been performed from wearable data. We propose an innovative approach to estimate SDNN24 only exploiting the Heart Rate (HR) that is normally available on wearable fitness trackers and less affected by data noise. The standard deviation of inter-beats intervals (SDNN24) and the standard deviation of the Average inter-beats intervals (ANN) derived from the HR (obtained in a time window with defined duration, i.e., 1, 5, 10, 30 and 60 min), i.e., ANN=60HR (SDANNHR24), were calculated over 24 h. Power spectrum analysis using the Lomb-Scargle Peridogram was performed to assess frequency domain HRV parameters (Ultra Low Frequency, Very Low Frequency, Low Frequency, and High Frequency). Due to the fact that SDNN24 reflects the total power of the power of the HRV spectrum, the values estimated from HR measures (SDANNHR24) underestimate the real values because of the high frequencies that are missing. Subjects with low and high cardiovascular risk show different power spectra. In particular, differences are detected in Ultra Low and Very Low frequencies, while similar results are shown in Low and High frequencies. For this reason, we found that HR measures contain enough information to discriminate cardiovascular risk. Semi-continuous measures of HR throughout 24 h, as measured by most wrist-worn fitness wearable devices, should be sufficient to estimate SDNN24 and cardiovascular risk.

Beating Rate Variability of Isolated Mammal Sinoatrial Node Tissue: Insight Into Its Contribution to Heart Rate Variability

Background

Because of the complexity of the interaction between the internal pacemaker mechanisms, cell interconnected signals, and interaction with other body systems, study of the role of individual systems must be performed under in vivo and in situ conditions. The in situ approach is valuable when exploring the mechanisms that govern the beating rate and rhythm of the sinoatrial node (SAN), the heart's primary pacemaker. SAN beating rate changes on a beat-to-beat basis. However, to date, there are no standard methods and tools for beating rate variability (BRV) analysis from electrograms (EGMs) collected from different mammals, and there is no centralized public database with such recordings.

Methods

We used EGM recordings obtained from control SAN tissues of rabbits (n = 9) and mice (n = 30) and from mouse SAN tissues (n = 6) that were exposed to drug intervention. The data were harnessed to develop a beat detector to derive the beat-to-beat interval time series from EGM recordings. We adapted BRV measures from heart rate variability and reported their range for rabbit and mouse.

Results

The beat detector algorithm performed with 99% accuracy, sensitivity, and positive predictive value on the test (mouse) and validation (rabbit and mouse) sets. Differences in the frequency band cutoff were found between BRV of SAN tissue vs. heart rate variability (HRV) of in vivo recordings. A significant reduction in power spectrum density existed in the high frequency band, and a relative increase was seen in the low and very low frequency bands. In isolated SAN, the larger animal had a slower beating rate but with lower BRV, which contrasted the phenomena reported for in vivo analysis. Thus, the non-linear inverse relationship between the average HR and HRV is not maintained under in situ conditions. The beat detector, BRV measures, and databases were contributed to the open-source PhysioZoo software (available at: https://physiozoo.com/).

Conclusion

Our approach will enable standardization and reproducibility of BRV analysis in mammals. Different trends were found between beating rate and BRV or HRV in isolated SAN tissue vs. recordings collected under in vivo conditions, respectively, implying a complex interaction between the SAN and the autonomic nervous system in determining HRV in vivo.

Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts

Application of ultra–short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people’s daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase.