Methods derived from nonlinear dynamics for analysing heart rate variability

Heart rate variability and overtraining in soccer players: A systematic review

This systematic review aims to determine if there is a correlation between heart rate variability (HRV) indices and overtraining symptoms (OTS) in soccer players. Conforming to PRISMA guidelines, a search was conducted in February 2024 on Web of Science, PubMed, EMBASE, CINAHL, and SCOPUS. Studies published in English investigating the relationship between HRV parameters and OTS in adult soccer players (>18 years) were included. Accepted study designs were randomized controlled trials, controlled clinical trials, longitudinal studies, prospective studies, cross-sectional studies, and retrospective studies. Methodological quality was assessed using the Joanna Briggs Institute (JBI) checklist. The search identified 2041 articles, with 19 included postscreening. Most studies examined the correlation between HRV and OTS using linear indices. The average JBI checklist score was 6.3, indicating fair methodological quality. Thirteen studies showed a relationship between linear HRV parameters and OTS, linked to performance/clinical tests, training load, adaptation, fatigue, recovery, or hormonal markers. Thirteen correlations involved HRV frequency domain parameters, and 28 involved HRV time domain characteristics. HRV indexes were linked to OTS markers such as physical performance and psychological aspects in soccer players. Standardization in research methodologies, addressing confounding factors, and exploring additional indexes are crucial in the future.

One-week test-retest stability of heart rate variability during rest and deep breathing

Objective. Heart rate variability (HRV) is an important indicator of cardiac autonomic function. Given its clinical significance, reliable HRV assessment is crucial. Here, we assessed test-retest stability, as a key aspect of reliability, quantifying the consistency of a measure when repeated under the same conditions.Approach. This observational study includes healthy individuals. A 20 min electrocardiogram was recorded at rest in a supine position and during deep breathing in two lab sessions within one week, at the same time of day. HRV indices from time domain, frequency domain, nonlinear dynamics, and information-theoretic complexity were assessed using a validated toolbox. Additionally, heart rate variations per respiratory cycle were evaluated during deep breathing. Lifestyle factors such as perceived stress, mood, physical activity, sleep quality were assessed prior to both sessions. Intra-class correlation (ICC) and coefficients of variation (CVs) were used to assess the concordance between the two measurements and the relative deviation, respectively.Main results. From 62 screened individuals, 51 participants were recruited from the local community. One participant opted out for personal reasons, and another with frequent premature beats was excluded, leaving a final sample of 49 individuals. Most self-rated psychological and lifestyle indicators showed substantial agreement, though participants reported less stress and better mood in the second session. At rest, ICC of HRV ranged from 0.50 to 0.83, with CV from 5% to 41%. Spectral HRV measures were less reliable than time domain parameters. Nonlinear and time domain features had substantial to nearly perfect agreement. Complexity measures had low CVs but limited test-retest correlation. The stability indices of HRV during deep breathing were not significantly different from those during rest. Test-retest differences in root mean square of the successive beat-to-beat interval difference were not sufficiently explained by lifestyle factors.Significance.Test-retest stability of HRV depends considerably on chosen measures. Our data suggest that HRV can be assessed reliably using time-domain indices at rest.

Investigation of Possible Sources of Electrodermal Activity in Surgical Personnel to Assess Workplace Stress Levels

OBJECTIVE

The aim of this study was to evaluate alternative measurement points to assess stress levels during surgery, given that taking electrodermal activity (EDA) measurements from the palms, a commonly used standard, is impractical in surgical contexts.

METHODS

A combination of mentally challenging tasks on a laparoscopy trainer (LapSim) and physically challenging sports exercises were performed by a group of participants. During these tasks, EDA was measured at three different locations: the fingers, toes, and shoulder/neck area. Additionally, an electrocardiogram was used as an objective measure of stress levels.

RESULTS

The findings indicated that EDA measurements taken at the toes produced similar high skin conductance levels to measurements taken at the palms. However, significantly lower skin conductance levels were observed at the shoulder. Despite this, the cross-correlation of EDA data revealed high correlation coefficients (above 0.96) between both the toe and shoulder measurements when compared to the palm data.

CONCLUSIONS

The study concludes that both the toes and the shoulder/neck area serve as viable alternative locations for assessing occupational stress levels in surgical personnel. This assertion is supported by the similarity of the data produced by these methods to that of the standard finger-based technique, with the latter still being identified as the most sensitive method for capturing EDA. Subsequently, these findings attest to the potential for practical adaptation of this technique in surgical contexts.

Transitions in growing networks using a structural complexity approach

Structure changes or transitions are common in growing networks (complex networks, graphs, etc.) and must be precisely determined. The introduced quantitative measure of the structural complexity of the network based on a procedure similar to the renormalization process allows one to reveal such changes. The proposed concept of the network structural complexity accounts for the difference between the actual and averaged network structures on different scales and corresponds to the qualitative comprehension of complexity. The structural complexity can be found for the weighted networks also. The structural complexities for various types of growing networks exhibiting transitions similar to phase transitions were found-the deterministic infinite and finite size artificial networks of different natures including percolation structures, and the time series of various types of cardiac rhythms mapped to complex networks using the parametric visibility graph algorithm. In all the cases the structural complexity of the growing network reaches a maximum near the transition point: the formation of a giant component in the graph or at the percolation threshold for two-dimensional and three-dimensional square lattices when a giant cluster having a fractal structure has emerged. Therefore, the structural complexity of the network allows us to detect and study processes similar to a second-order phase transition in complex networks. The structural complexity of a network node can serve as a kind of centrality index, auxiliary, or generalization to the local clustering coefficient. Such an index provides another new ranking manner for the network nodes. Being an easily computable measure, the network structural complexity might help to reveal different features of complex systems and processes of the real world.

Publication guidelines for human heart rate and heart rate variability studies in psychophysiology-Part 1: Physiological underpinnings and foundations of measurement

This Committee Report provides methodological, interpretive, and reporting guidance for researchers who use measures of heart rate (HR) and heart rate variability (HRV) in psychophysiological research. We provide brief summaries of best practices in measuring HR and HRV via electrocardiographic and photoplethysmographic signals in laboratory, field (ambulatory), and brain-imaging contexts to address research questions incorporating measures of HR and HRV. The Report emphasizes evidence for the strengths and weaknesses of different recording and derivation methods for measures of HR and HRV. Along with this guidance, the Report reviews what is known about the origin of the heartbeat and its neural control, including factors that produce and influence HRV metrics. The Report concludes with checklists to guide authors in study design and analysis considerations, as well as guidance on the reporting of key methodological details and characteristics of the samples under study. It is expected that rigorous and transparent recording and reporting of HR and HRV measures will strengthen inferences across the many applications of these metrics in psychophysiology. The prior Committee Reports on HR and HRV are several decades old. Since their appearance, technologies for human cardiac and vascular monitoring in laboratory and daily life (i.e., ambulatory) contexts have greatly expanded. This Committee Report was prepared for the Society for Psychophysiological Research to provide updated methodological and interpretive guidance, as well as to summarize best practices for reporting HR and HRV studies in humans.

Update: factors influencing heart rate variability-a narrative review

Objective

Heart rate variability (HRV) is an important non-invasive marker for the assessment of an organism's autonomic physiological regulatory pathways. Lower HRV has been shown to correlate with increased mortality. HRV is influenced by various factors or diseases. The aim of this narrative review is to describe the current state of knowledge on factors influencing HRV and their significance for interpretation.

Methods

The narrative review only included reviews, meta-analyses, and cohort studies which were published until 2021. HRV confounders were grouped into four categories (non-influenceable physiological factors, diseases, influenceable lifestyle factors and external factors).

Results

The review found that HRV was decreased not only in non-influenceable physiological factors (e.g., age, gender, ethnicity) but also in connection with various number of acute and chronic diseases (e.g., psychiatric diseases, myocardial infarction, heart failure), influenceable lifestyle factors (e.g., alcohol abuse, overweight, physical activity), and external factors (e.g., heat, noise, shift work, harmful- and hazardous substances).

Conclusion

In order to improve the quality of HRV studies and to ensure accurate interpretation, it is recommended that confounders be taken into account in future diagnostic measurements or measurements in the workplace (e.g., as part of health promotion measures) in order to counteract data bias.

Exploring Cardiopulmonary Interactions: A Novel Phase-Amplitude Coupling Method

The cardiopulmonary coupling (CPC) involves complex interactions between the autonomic nervous system and respiratory control, affecting heart and respiratory rates. This study introduces a novel phase-amplitude coupling (PAC) method using the direct modulation index (dMI) for analyzing the bidirectional interactions between the heart rate variability and respiration of participants listening to classical music. The Empirical Mode Decomposition (EMD) was employed to decompose the RR intervals and the respiration signals, and the dMI-based PAC is then applied to the decomposed IMFs. The results demonstrate a notable enhancement in RESP-RR interactions during music exposure, indicating a stronger influence of respiratory patterns on heart rate during this relaxed state. Additionally, a pronounced cardiopulmonary coupling was observed in the low-frequency bands under conditions of listening to music. These findings highlight the complex, bidirectional interactions between breathing and heart rate. The dMI method emerges as a robust, noise-resistant analytical tool with promising implications for cardiovascular research and potential clinical applications.

Guideline for the application of heart rate and heart rate variability in occupational medicine and occupational health science

This updated guideline replaces the "Guideline for the application of heart rate and heart rate variability in occupational medicine and occupational health science" first published in 2014. Based on the older version of the guideline, the authors have reviewed and evaluated the findings on the use of heart rate (HR) and heart rate variability (HRV) that have been published in the meantime and incorporated them into a new version of this guideline.This guideline was developed for application in clinical practice and research purposes in the fields of occupational medicine and occupational science to complement evaluation procedures with respect to exposure and risk assessment at the workplace by the use of objective physiological workload indicators. In addition, HRV is also suitable for assessing the state of health and for monitoring the progress of illnesses and preventive medical measures. It gives an overview of factors influencing the regulation of the HR and HRV at rest and during work. It further illustrates methods for measuring and analyzing these parameters under standardized laboratory and real workload conditions, areas of application as well as the quality control procedures to be followed during the recording and evaluation of HR and HRV.

Spectral Analysis of Heart Rate Variability in Time-Varying Conditions and in the Presence of Confounding Factors

The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time-frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from a mathematical/statistical model, suggesting that the model can be used to simulate data used for performance evaluation. The inclusion of a respiratory signal, whether measured or derived, is another feature of many recent methods, e.g., used to guide the decomposition of the HRV signal so that signals related as well as unrelated to respiration can be analyzed. It is concluded that the development of new approaches to handling time-varying scenarios are warranted, as is benchmarking of performance evaluated in technical as well as in physiological/clinical terms.

Comprehensive viewpoints on heart rate variability at high altitude

OBJECTIVES

Hypoxia is a physiological state characterized by reduced oxygen levels in organs and tissues. It is a common clinicopathological process and a major cause of health problems in highland areas.  Heart rate variability (HRV) is a measure of the balance in autonomic innervation to the heart. It provides valuable information on the regulation of the cardiovascular system by neurohumoral factors, and changes in HRV reflect the complex interactions between multiple systems. In this review, we provide a comprehensive overview of the relationship between high-altitude hypoxia and HRV. We summarize the different mechanisms of diseases caused by hypoxia and explore the changes in HRV across various systems. Additionally, we discuss relevant pharmaceutical interventions. Overall, this review aims to provide research ideas and assistance for in-depth studies on HRV. By understanding the intricate relationship between high-altitude hypoxia and HRV, we can gain insights into the underlying mechanisms and potential therapeutic approaches to mitigate the effects of hypoxia on cardiovascular and other systems.

METHODS

The relevant literature was collected systematically from scientific database, including PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Baidu Scholar, as well as other literature sources, such as classic books of hypoxia.

RESULTS

There is a close relationship between heart rate variability and high-altitude hypoxia. Heart rate variability is an indicator that evaluates the impact of hypoxia on the cardiovascular system and other related systems. By improving the observation of HRV, we can estimate the progress of cardiovascular diseases and predict the impact on other systems related to cardiovascular health. At the same time, changes in heart rate variability can be used to observe the efficacy of preventive drugs for altitude related diseases.

CONCLUSIONS

HRV can be used to assess autonomic nervous function under various systemic conditions, and can be used to predict and monitor diseases caused by hypoxia at high altitude. Investigating the correlation between high altitude hypoxia and heart rate variability can help make HRV more rapid, accurate, and effective for the diagnosis of plateau-related diseases.

Linear and non‑linear indices of vagal nerve in relation to sex and inflammation in patients with Covid‑19

Hyperinflammation is one of the most important pathophysiological risk factors for poor prognosis in patients with coronavirus disease-2019 (Covid-19). Low vagal neuro-immune modulation can lead into this kind of immune dysregulation. The association between vagal activity, sex and inflammatory markers were investigated in patients with Covid-19. A total of 19 patients with Covid-19 were included in the present study. Vagus nerve activity was indexed by heart rate variability (HRV) derived from electrocardiogram at hospital admission. Linear HRV parameters included the root mean square of successive RR interval differences (RMSSD) and high-frequency HRV (HF-HRV), while non-linear parameters included 2 UV%. Immune/inflammatory parameters included C-reactive protein (CRP), interleukin-6 (IL-6), neutrophil/lymphocyte ratio (NLR), systemic inflammatory index (SII), and procalcitonin (PCT). It has been revealed that both linear HRV indices HF-HRV and RMSSD, are significantly negatively correlated with CRP and IL-6, independent of age. The non-linear index of 2 UV% is significantly negatively correlated with NLR and SII, which reflect subtle changes in the response of immunocompetent cells. Patients that received high-flow nasal oxygen therapy had significantly higher IL-6 and CRP levels and lower levels of HF-HRV and RMSSD. These patients also had a significantly longer length of stay in hospital (LOS) than patients receiving low-flow oxygen therapy. Men had higher plasma PCT levels and longer LOS in hospital than women, and PCT statistically explained (mediated) the association between sex and LOS. The present study showed different correlations of linear and non-linear vagal indexes of HRV and inflammatory markers in patients with Covid-19. Significant sex differences in certain inflammatory markers were also observed, which may very well verify previous findings of poor prognosis in men with Covid-19. HRV reflects a continuous interaction between the sympathetic and parasympathetic autonomic nervous systems, which are affected by mental or physical stress, and certain disease states. The increased sympathetic and decreased parasympathetic vagal tone contribute to a higher risk of diseases associated with inflammation, cardiovascular disease, cancer, pulmonary diseases and other pathologies, including infectious diseases such as Covid-19. The present study showed that higher RMSSD (a marker of vagal activity) in Covid-19 patients is associated with lower levels of inflammatory biomarkers, a lower need for treatment and is negatively correlated with intensive care unit admission, leading to a shorter hospital stay. These findings support the idea that activation of vagus nerve may help certain Covid-19 patients by reducing the cytokine storm and excessive inflammation.

Neuropathy in prediabetes

Diabetic neuropathies are the most frequent complications of diabetes, contributing to high morbidity, excess mortality, reduced quality of life, and increased healthcare costs. Prediabetes is characterised by glucose levels within an intermediate range above normoglycaemia yet below the diagnostic threshold for diabetes. In 2021, 10.6% and 6.2% of adults worldwide were estimated to have impaired glucose tolerance and impaired fasting glucose, respectively, the majority of whom are unaware of having prediabetes. Evidence has accumulated suggesting that prediabetes is a predictor of cardiovascular disease (CVD) and increased mortality. The past 2 decades have witnessed an extensive debate, particularly among diabetologists and neurologists, as to whether prediabetes is associated with peripheral neuropathy. In this review, we elaborate on the current evidence, particularly from population-based studies supporting an increased risk of distal sensorimotor polyneuropathy (DSPN) and cardiovascular autonomic neuropathy (CAN) in people with prediabetes. Moreover, we discuss whether lifestyle interventions showing efficacy in preventing or delaying the transition from prediabetes to diabetes in persons with prediabetes may also exert favourable effects on the development and progression of DSPN and CAN. This review should help in raising the awareness of and translating the current knowledge on neuropathies in people with prediabetes into clinical practice and public health. The current recommendation that adults who are overweight or obese should be screened for prediabetes and referred to or offered preventive interventions should ultimately culminate in preventing not only CVD but also prediabetic neuropathy.

Exploring the mechanisms underlying stroke volume variability reduction in a murine model of heart failure with reduced ejection fraction

Heart failure with reduced ejection fraction (HFrEF) is accompanied by disregulation of cardiovascular function. Heart rate variability (HRV) is commonly used to assess autonomic dysfunction in HFrEF. However, analysis of stroke volume variability (SVV) may provide additional insights. We examined HRV and SVV in a mouse model of HFrEF. HFrEF mice exhibited reduced stroke volume and ejection fraction versus controls, confirming cardiac contractile dysfunction. HRV was preserved in HFrEF mice. However, SVV was markedly diminished, indicating dissociation between HRV and SVV regulation. Using a mathematical model, we propose that Frank-Starling mechanism abnormalities in HFrEF disrupt SVV independent of HRV. Assessing SVV could thus provide unique insights beyond HRV into cardiovascular control deficits in HFrEF.

Multifractal foundations of biomarker discovery for heart disease and stroke

Any reliable biomarker has to be specific, generalizable, and reproducible across individuals and contexts. The exact values of such a biomarker must represent similar health states in different individuals and at different times within the same individual to result in the minimum possible false-positive and false-negative rates. The application of standard cut-off points and risk scores across populations hinges upon the assumption of such generalizability. Such generalizability, in turn, hinges upon this condition that the phenomenon investigated by current statistical methods is ergodic, i.e., its statistical measures converge over individuals and time within the finite limit of observations. However, emerging evidence indicates that biological processes abound with nonergodicity, threatening this generalizability. Here, we present a solution for how to make generalizable inferences by deriving ergodic descriptions of nonergodic phenomena. For this aim, we proposed capturing the origin of ergodicity-breaking in many biological processes: cascade dynamics. To assess our hypotheses, we embraced the challenge of identifying reliable biomarkers for heart disease and stroke, which, despite being the leading cause of death worldwide and decades of research, lacks reliable biomarkers and risk stratification tools. We showed that raw R-R interval data and its common descriptors based on mean and variance are nonergodic and non-specific. On the other hand, the cascade-dynamical descriptors, the Hurst exponent encoding linear temporal correlations, and multifractal nonlinearity encoding nonlinear interactions across scales described the nonergodic heart rate variability more ergodically and were specific. This study inaugurates applying the critical concept of ergodicity in discovering and applying digital biomarkers of health and disease.

Heart Rate Variability for Evaluating Psychological Stress Changes in Healthy Adults: A Scoping Review

The utility of heart rate variability (HRV) for characterizing psychological stress is primarily impacted by methodological considerations such as study populations, experienced versus induced stress, and method of stress assessment. Here, we review studies on the associations between HRV and psychological stress, examining the nature of stress, ways stress was assessed, and HRV metrics used. The review was performed according to the PRISMA guidelines on select databases. Studies that examined the HRV-stress relationship via repeated measurements and validated psychometric instruments were included (n = 15). Participant numbers and ages ranged between 10 and 403 subjects and 18 and 60 years, respectively. Both experimental (n = 9) and real-life stress (n = 6) have been explored. While RMSSD was the most reported HRV metric (n = 10) significantly associated with stress, other metrics, including LF/HF (n = 7) and HF power (n = 6) were also reported. Various linear and nonlinear HRV metrics have been utilized, with nonlinear metrics used less often. The most frequently used psychometric instrument was the State-Trait Anxiety Inventory (n = 10), though various other instruments have been reported. In conclusion, HRV is a valid measure of the psychological stress response. Standard stress induction and assessment protocols combined with validated HRV measures in different domains will improve the validity of findings.

Electromagnetic fields - do they pose a cardiovascular risk?

Mobile wireless communication technologies have now become an everyday part of our lives, 24 hours a day, 7 days a week. Monitoring the autonomous system under exposition to electromagnetic fields may play an important role in broading of our still limited knowledge on their effect on human body. Thus, we studied the interaction of the high frequency electromagnetic field (HF EMF) with living body and its effect on the autonomic control of heart rate using Heart Rate Variability (HRV) linear and nonlinear analyses in healthy volunteers. A group of young healthy probands (n=30, age mean: 24.2 ± 3.5 years) without any symptoms of disease was exposed to EMF with f=2400 MHz (Wi Fi), and f=2600 MHz (4G) for 5 minutes applied on the chest area. The short-term heart rate variability (HRV) metrics were used as an indicator of complex cardiac autonomic control. The evaluated HRV parameters: RR interval (ms), high frequency spectral power (HF-HRV in [ln(ms2)]) as an index of cardiovagal control, and a symbolic dynamic index of 0V %, indicating cardiac sympathetic activity. The cardiac-linked parasympathetic index HF-HRV was significantly reduced (p =0.036) and sympathetically mediated HRV index 0V % was significantly higher (p=0.002) during EMF exposure at 2400 MHz (Wi-Fi), compared to simulated 4G frequency 2600 MHz. No significant differences were found in the RR intervals. Our results revealed a shift in cardiac autonomic regulation towards sympathetic overactivity and parasympathetic underactivity indexed by HRV parameters during EMF exposure in young healthy persons. It seems that HF EMF exposure results in abnormal complex cardiac autonomic regulatory integrity which may be associated with higher risk of later cardiovascular complications already in healthy probands.

Visibility Graph Analysis of Heartbeat Time Series: Comparison of Young vs. Old, Healthy vs. Diseased, Rest vs. Exercise, and Sedentary vs. Active

Using the visibility graph algorithm (VGA), a complex network can be associated with a time series, such that the properties of the time series can be obtained by studying those of the network. Any value of the time series becomes a node of the network, and the number of other nodes that it is connected to can be quantified. The degree of connectivity of a node is positively correlated with its magnitude. The slope of the regression line is denoted by k-M, and, in this work, this parameter was calculated for the cardiac interbeat time series of different contrasting groups, namely: young vs. elderly; healthy subjects vs. patients with congestive heart failure (CHF); young subjects and adults at rest vs. exercising young subjects and adults; and, finally, sedentary young subjects and adults vs. active young subjects and adults. In addition, other network parameters, including the average degree and the average path length, of these time series networks were also analyzed. Significant differences were observed in the k-M parameter, average degree, and average path length for all analyzed groups. This methodology based on the analysis of the three mentioned parameters of complex networks has the advantage that such parameters are very easy to calculate, and it is useful to classify heartbeat time series of subjects with CHF vs. healthy subjects, and also for young vs. elderly subjects and sedentary vs. active subjects.

The Evaluation of Emotional Intelligence by the Analysis of Heart Rate Variability

Emotional intelligence (EI) is a critical social intelligence skill that refers to an individual's ability to assess their own emotions and those of others. While EI has been shown to predict an individual's productivity, personal success, and ability to maintain positive relationships, its assessment has primarily relied on subjective reports, which are vulnerable to response distortion and limit the validity of the assessment. To address this limitation, we propose a novel method for assessing EI based on physiological responses-specifically heart rate variability (HRV) and dynamics. We conducted four experiments to develop this method. First, we designed, analyzed, and selected photos to evaluate the ability to recognize emotions. Second, we produced and selected facial expression stimuli (i.e., avatars) that were standardized based on a two-dimensional model. Third, we obtained physiological response data (HRV and dynamics) from participants as they viewed the photos and avatars. Finally, we analyzed HRV measures to produce an evaluation criterion for assessing EI. Results showed that participants' low and high EI could be discriminated based on the number of HRV indices that were statistically different between the two groups. Specifically, 14 HRV indices, including HF (high-frequency power), lnHF (the natural logarithm of HF), and RSA (respiratory sinus arrhythmia), were significant markers for discerning between low and high EI groups. Our method has implications for improving the validity of EI assessment by providing objective and quantifiable measures that are less vulnerable to response distortion.

Physical Exercise Effects on Cardiovascular Autonomic Modulation in Postmenopausal Women-A Systematic Review and Meta-Analysis

BACKGROUND

The cardioprotective effect of physical exercise has been demonstrated in several studies. However, no systematic or updated analysis has described the effects of physical exercise on cardiovascular autonomic modulation in postmenopausal women.

AIM

to describe the effects of physical exercise on cardiovascular autonomic modulation in postmenopausal women.

METHODS

The Scopus, PubMed, and Embase databases were searched for randomized clinical trials published between January 2011 and December 2021, and regarding the effects of physical exercise on cardiovascular autonomic modulation in postmenopausal women. Two independent authors processed the citations. The methodological quality was evaluated using the PEDRo scale.

RESULTS

Of the 91 studies identified, only 8 met the inclusion criteria, of which 7 had fair or poor methodological quality. The analyzed studies investigated the effects of functional training, whole-body vibration, muscular resistance, stretching, and aerobic exercises performed at home or at the gym. The majority of these exercise modalities showed improvements in heart-rate variability (HRV) indices and in the low-frequency band of blood pressure variability. The meta-analysis shows that exercise increased the standard deviation of instantaneous beat-to-beat variability (SD1) (mean difference (MD) = 3.99; 95% confidence interval (CI) = 1.22 to 6.77, n = 46; I²: 0%) and the standard deviation of long-term variability (SD2) (MD = 11.37; 95% CI = 2.99 to 19.75; n = 46; I²: 0%).

CONCLUSIONS

Aerobic exercise and some nonconventional training modalities may have beneficial effects on cardiovascular autonomic modulation in postmenopausal women. More high-quality studies are still needed to further confirm their efficacy and safety.

Heart Rate Complexity and Autonomic Modulation Are Associated with Psychological Response Inhibition in Healthy Subjects

BACKGROUND

the ability to suppress/regulate impulsive reactions has been identified as common factor underlying the performance in all executive function tasks. We analyzed the HRV signals (power of high (HF) and low (LF) frequency, Sample Entropy (SampEn), and Complexity Index (CI)) during the execution of cognitive tests to assess flexibility, inhibition abilities, and rule learning.

METHODS

we enrolled thirty-six healthy subjects, recording five minutes of resting state and two tasks of increasing complexity based on 220 visual stimuli with 12 × 12 cm red and white squares on a black background.

RESULTS

at baseline, CI was negatively correlated with age, and LF was negatively correlated with SampEn. In Task 1, the CI and LF/HF were negatively correlated with errors. In Task 2, the reaction time positively correlated with the CI and the LF/HF ratio errors. Using a binary logistic regression model, age, CI, and LF/HF ratio classified performance groups with a sensitivity and specificity of 73 and 71%, respectively.

CONCLUSIONS

this study performed an important initial exploration in defining the complex relationship between CI, sympathovagal balance, and age in regulating impulsive reactions during cognitive tests. Our approach could be applied in assessing cognitive decline, providing additional information on the brain-heart interaction.

Heart Rate Variability in Individuals with Down Syndrome: A Scoping Review with Methodological Considerations

Individuals with Down syndrome (DS) present similar heart rate variability (HRV) parameters at rest but different responses to selected movement maneuvers in comparison to individuals without DS, which indicates reduced vagal regulation. The present study undertakes a scoping review of research on HRV in individuals with DS, with special attention paid to the compliance of the studies with standards and methodological paper guidelines for HRV assessment and interpretation. A review was performed using PubMed, Web of Science and CINAHL databases to search for English language publications from 1996 to 2020 with the MESH terms "heart rate variability" and "down syndrome", with the additional inclusion criteria of including only human participants and empirical investigations. From 74 studies, 15 were included in the review. None of the reviewed studies met the recommendations laid out by the standards and guidelines for providing the acquisition of RR intervals and necessary details on HRV analysis. Since authors publishing papers on this research topic do not adhere to the prescribed standards and guidelines when constructing the methodology, results of the research papers on the topic are not directly comparable. Authors need to design the study methodology more robustly by following the aforementioned standards, guidelines and recommendations.

Association of heartbeat complexity with survival in advanced non-small cell lung cancer patients

Background

Previous studies have shown that the predictive value of traditional linear (time domain and frequency domain) heart rate variability (HRV) for the survival of patients with advanced non-small cell lung cancer (NSCLC) is controversial. Nonlinear methods, based on the concept of complexity, have been used to evaluate HRV, providing a new means to reveal the physiological and pathological changes in HRV. This study aimed to assess the association between heartbeat complexity and overall survival in patients with advanced NSCLC.

Methods

This study included 78 patients with advanced NSCLC (mean age: 62.0 ± 9.3 years). A 5-min resting electrocardiogram of advanced NSCLC patients was collected to analyze the following HRV parameters: time domain indicators, i.e., standard deviation of the normal-normal intervals (SDNN) and root mean square of successive interval differences (RMSSD); frequency domain indicators, i.e., total power (TP), low frequency power (LF), high frequency power (HF), and the ratio of LF to HF (LF/HF); nonlinear HRV indicators characterizing heartbeat complexity, i.e., approximate entropy (ApEn), sample entropy (SampEn), and recurrence quantification analysis (RQA) indexes: mean diagonal line length (Lmean), maximal diagonal line length (Lmax), recurrence rate (REC), determinism (DET), and shannon entropy (ShanEn).

Results

Univariate analysis revealed that the linear frequency domain parameter HF and nonlinear RQA parameters Lmax, REC, and DET were significantly correlated with the survival of advanced NSCLC patients (all p < 0.05). After adjusting for confounders in the multivariate analysis, HF, REC, and DET were found to be independent prognostic factors for the survival of patients with advanced NSCLC (all p < 0.05).

Conclusion

There was an independent association between heartbeat complexity and survival in advanced NSCLC patients. The nonlinear analysis method based on RQA may provide valuable additional information for the prognostic stratification of patients with advanced NSCLC and may supplement the traditional time domain and frequency domain analysis methods.

Detecting driver fatigue using heart rate variability: A systematic review

Driver fatigue detection systems have potential to improve road safety by preventing crashes and saving lives. Conventional driver monitoring systems based on driving performance and facial features may be challenged by the application of automated driving systems. This limitation could potentially be overcome by monitoring systems based on physiological measurements. Heart rate variability (HRV) is a physiological marker of interest for detecting driver fatigue that can be measured during real life driving. This systematic review investigates the relationship between HRV measures and driver fatigue, as well as the performance of HRV based fatigue detection systems. With the applied eligibility criteria, 18 articles were identified in this review. Inconsistent results can be found within the studies that investigated differences of HRV measures between alert and fatigued drivers. For studies that developed HRV based fatigue detection systems, the detection performance showed a large variation, where the detection accuracy ranged from 44% to 100%. The inconsistency and variation of the results can be caused by differences in several key aspects in the study designs. Progress in this field is needed to determine the relationship between HRV and different fatigue causal factors and its connection to driver performance. To be deployed, HRV-based fatigue detection systems need to be thoroughly tested in real life conditions with good coverage of relevant driving scenarios and a sufficient number of participants.

Ear your heart: transcutaneous auricular vagus nerve stimulation on heart rate variability in healthy young participants

BACKGROUND

Transcutaneous auricular vagus nerve stimulation (taVNS) stimulating the auricular branch of the vagus nerve along a well-defined neuroanatomical pathway, has promising therapeutic efficacy. Potentially, taVNS can modulate autonomic responses. Specifically, taVNS can induce more consistent parasympathetic activation and may lead to increased heart rate variability (HRV). However, the effects of taVNS on HRV remain inconclusive. Here, we investigated changes in HRV due to brief alteration periods of parasympathetic-vagal cardiac activity produced by taVNS on the cymba as opposed to control administration via the helix.

MATERIALS AND METHODS

We compared the effect of 10 min of active stimulation (i.e., cymba conchae) to sham stimulation (i.e., helix) on peripheral cardiovascular response, in 28 healthy young adults. HRV was estimated in the time domain and frequency domain during the overall stimulation.

RESULTS

Although active-taVNS and sham-taVNS stimulation did not differ in subjective intensity ratings, the active stimulation of the cymba led to vagally mediated HRV increases in both the time and frequency domains. Differences were significant between active-taVNS and both sham-taVNS and resting conditions in the absence of stimulation for various HRV parameters, but not for the low-frequency index of HRV, where no differences were found between active-taVNS and sham-taVNS conditions.

CONCLUSION

This work supports the hypothesis that taVNS reliably induces a rapid increase in HRV parameters when auricular stimulation is used to recruit fibers in the cymba compared to stimulation at another site. The results suggest that HRV can be used as a physiological indicator of autonomic tone in taVNS for research and potential therapeutic applications, in line with the established effects of invasive VNS. Knowledge of the physiological effect of taVNS short sessions in modulating cardiovagal processing is essential for enhancing its clinical use.

Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review

Heart rate time series are widely used to characterize physiological states and athletic performance. Among the main indicators of metabolic and physiological states, the detection of metabolic thresholds is an important tool in establishing training protocols in both sport and clinical fields. This paper reviews the most common methods, applied to heart rate (HR) time series, aiming to detect metabolic thresholds. These methodologies have been largely used to assess energy metabolism and to identify the appropriate intensity of physical exercise which can reduce body weight and improve physical fitness. Specifically, we focused on the main nonlinear signal evaluation methods using HR to identify metabolic thresholds with the purpose of identifying a method which can represent a useful tool for the real-time settings of wearable devices in sport activities. While the advantages and disadvantages of each method, and the possible applications, are presented, this review confirms that the nonlinear analysis of HR time series represents a solid, robust and noninvasive approach to assess metabolic thresholds.

Non-invasive prognostic biomarker of lung cancer patients with brain metastases: Recurrence quantification analysis of heart rate variability

Background: It has previously been shown that the time-domain characteristic of heart rate variability (HRV) is an independent prognostic factor for lung cancer patients with brain metastasis (LCBM). However, it is unclear whether the nonlinear dynamic features contained in HRV are associated with prognosis in patients with LCBM. Recurrence quantification analysis (RQA) is a common nonlinear method used to characterize the complexity of heartbeat interval time series. This study was aimed to explore the association between HRV RQA parameters and prognosis in LCBM patients.

Methods: Fifty-six LCBM patients from the Department of Radiation Oncology, the First Affiliated Hospital of Bengbu Medical College, were enrolled in this study. Five-minute ECG data were collected by a mini-ECG recorder before the first brain radiotherapy, and then heartbeat interval time series were extracted for RQA. The main parameters included the mean diagonal line length (Lmean), maximal diagonal line length (Lmax), percent of recurrence (REC), determinism (DET) and Shannon entropy (ShanEn). Patients were followed up (the average follow-up time was 19.2 months, a total of 37 patients died), and the relationships between the RQA parameters and survival of LCBM patients were evaluated by survival analysis.

Results: The univariate analysis showed that an Lmax of >376 beats portended worse survival in LCBM patients. Multivariate Cox regression analysis revealed that the Lmax was still an independent prognostic factor for patients with LCBM after adjusting for confounders such as the Karnofsky performance status (KPS) (HR = 0.318, 95% CI: 0.151–0.669, p = 0.003).

Conclusion: Reduced heartbeat complexity indicates a shorter survival time in patients with LCBM. As a non-invasive biomarker, RQA has the potential for application in evaluating the prognosis of LCBM patients.

Increased sympathetic modulation in breast cancer survivors determined by measurement of heart rate variability

Experimental and clinical studies have shown that the sympathetic nervous system (SNS) stimulates cancer progression and reduces the efficacy of oncological treatment. These effects may be reduced by pharmacological and psychotherapeutical approaches attenuating SNS tone. Therefore, it is necessary to identify those cancer survivors whose sympathetic modulation is excessively increased. For determination of SNS modulation, non-invasive method of heart rate variability (HRV) is widely used. In our study, HRV was determined from 5-min heartbeat recordings in healthy volunteers and in women with benign or malignant breast neoplasias, both in newly diagnosed patients and in women after initial treatment. We showed impaired cardio-vagal regulation in breast cancer patients (linear methods) and also found the increased sympathetic modulation indicated by the non-linear (the symbolic dynamics 0V%) parameter. This non-linear HRV analysis seems to be more sensitive than the linear one, indicating significant differences also in survivors after initial therapy in comparison to healthy controls. The lower sample entropy revealed reduced complexity in heart rate control in both breast cancer survivors groups. These findings suggest that HRV detection represents an inexpensive, easy, and reliable method for identification of those patients with breast cancer whose sympathetic modulation is significantly increased and in which the interventions, aimed at normalizing the balance in the autonomic nervous system (e.g. psychotherapy, biofeedback, treatment by β-blockers) may be the most effective.

Heart rate variability as a biomarker in patients with Chronic Chagas Cardiomyopathy with or without concomitant digestive involvement and its relationship with the Rassi score

Background

Dysautonomia plays an ancillary role in the pathogenesis of Chronic Chagas Cardiomyopathy (CCC), but is the key factor causing digestive organic involvement. We investigated the ability of heart rate variability (HRV) for death risk stratification in CCC and compared alterations of HRV in patients with isolated CCC and in those with the mixed form (CCC + digestive involvement). Thirty-one patients with CCC were classified into three risk groups (low, intermediate and high) according to their Rassi score. A single-lead ECG was recorded for a period of 10–20 min, RR series were generated and 31 HRV indices were calculated. The HRV was compared among the three risk groups and regarding the associated digestive involvement. Four machine learning models were created to predict the risk class of patients.

Results

Phase entropy is decreased and the percentage of inflection points is increased in patients from the high-, compared to the low-risk group. Fourteen patients had the mixed form, showing decreased triangular interpolation of the RR histogram and absolute power at the low-frequency band. The best predictive risk model was obtained by the support vector machine algorithm (overall F1-score of 0.61).

Conclusions

The mixed form of Chagas' disease showed a decrease in the slow HRV components. The worst prognosis in CCC is associated with increased heart rate fragmentation. The combination of HRV indices enhanced the accuracy of risk stratification. In patients with the mixed form of Chagas disease, a higher degree of sympathetic autonomic denervation may be associated with parasympathetic impairment.

Randomized Pilot Trial Using External Yarrow Liver Compress Applications With Metastatic Cancer Patients Suffering From Fatigue: Evaluation of Sympathetic Modulation by Heart Rate Variability Analysis

Background:

Liver compresses are commonly applied in complementary medicine during cancer treatment and are believed to have an energizing effect. We conducted a pilot study to investigate the influence of external application of yarrow liver compresses on the autonomous nervous system by analyzing the heart rate variability (HRV) in metastatic cancer patients undergoing radiation therapy and suffering from cancer-related fatigue (CRF).

Methods:

A randomized pilot trial was undertaken with patients suffering from CRF receiving palliative radiation therapy for bone or brain metastasis over 2 weeks. CRF patients were randomized into an intervention group receiving yarrow liver compresses and a control group receiving no external application. HRV was analyzed at the beginning (T1) and end (T2) period of the study during daytime (d) and nighttime (n) in both groups and quantified using time-, frequency-, and nonlinear dynamics domains.

Results:

A total of 39 patients were randomized between September 2017 and August 2019 and a total of 20 patients (10 per group) were available for analysis. For both groups, significant differences in HRV analyses were found in the comparison over the timeline between the first and second measurement (T1d→T2d) during daytime. Specifically, the intervention group showed decreased HRV and complexity of heart rate regulation, indicating increased sympathetic activity at time T2d, whereas the control group showed increased HRV and complexity of heart rate regulation, indicating increased vagal activity at time T2d. Furthermore, the observed HRV analyses showed almost no significant differences between the 2 groups in a direct comparison at the beginning and the end of the study (exception: T2d).

Conclusions:

Yarrow liver compresses led to increased sympathetic activity during daytime in the intervention group, whereas in the control group, which did not receive any external application, increased parasympathetic activity was observed.

Heart Rate Variability Analysis for Seizure Detection in Neonatal Intensive Care Units

In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely clinical intervention. Over the years, several neonatal seizure detection systems were proposed to detect neonatal seizures automatically and speed up seizure diagnosis, most based on the EEG signal analysis. Recently, research has focused on other possible seizure markers, such as electrocardiography (ECG). This work proposes an ECG-based NSD system to investigate the usefulness of heart rate variability (HRV) analysis to detect neonatal seizures in the NICUs. HRV analysis is performed considering time-domain, frequency-domain, entropy and multiscale entropy features. The performance is evaluated on a dataset of ECG signals from 51 full-term babies, 29 seizure-free. The proposed system gives results comparable to those reported in the literature: Area Under the Receiver Operating Characteristic Curve = 62%, Sensitivity = 47%, Specificity = 67%. Moreover, the system’s performance is evaluated in a real clinical environment, inevitably affected by several artefacts. To the best of our knowledge, our study proposes for the first time a multi-feature ECG-based NSD system that also offers a comparative analysis between babies suffering from seizures and seizure-free ones.

Does the Heart Fall Asleep?-Diurnal Variations in Heart Rate Variability in Patients with Disorders of Consciousness

The current study investigated heart rate (HR) and heart rate variability (HRV) across day and night in patients with disorders of consciousness (DOC). We recorded 24-h electrocardiography in 26 patients with DOC (i.e., unresponsive wakefulness syndrome (UWS; n = 16) and (exit) minimally conscious state ((E)MCS; n = 10)). To examine diurnal variations, HR and HRV indices in the time, frequency, and entropy domains were computed for periods of clear day- (forenoon: 8 a.m.-2 p.m.; afternoon: 2 p.m.-8 p.m.) and nighttime (11 p.m.-5 a.m.). The results indicate that patients' interbeat intervals (IBIs) were larger during the night than during the day, indicating HR slowing. The patients in UWS showed larger IBIs compared to the patients in (E)MCS, and the patients with non-traumatic brain injury showed lower HRV entropy than the patients with traumatic brain injury. Additionally, higher HRV entropy was associated with higher EEG entropy during the night. Thus, cardiac activity varies with a diurnal pattern in patients with DOC and can differentiate between patients' diagnoses and etiologies. Moreover, the interaction of heart and brain appears to follow a diurnal rhythm. Thus, HR and HRV seem to mirror the integrity of brain functioning and, consequently, might serve as supplementary measures for improving the validity of assessments in patients with DOC.

The Movesense Medical Sensor Chest Belt Device as Single Channel ECG for RR Interval Detection and HRV Analysis during Resting State and Incremental Exercise: A Cross-Sectional Validation Study

The value of heart rate variability (HRV) in the fields of health, disease, and exercise science has been established through numerous investigations. The typical mobile-based HRV device simply records interbeat intervals, without differentiation between noise or arrythmia as can be done with an electrocardiogram (ECG). The intent of this report is to validate a new single channel ECG device, the Movesense Medical sensor, against a conventional 12 channel ECG. A heterogeneous group of 21 participants performed an incremental cycling ramp to failure with measurements of HRV, before (PRE), during (EX), and after (POST). Results showed excellent correlations between devices for linear indexes with Pearson's r between 0.98 to 1.0 for meanRR, SDNN, RMSSD, and 0.95 to 0.97 for the non-linear index DFA a1 during PRE, EX, and POST. There was no significant difference in device specific meanRR during PRE and POST. Bland-Altman analysis showed high agreement between devices (PRE and POST: meanRR bias of 0.0 and 0.4 ms, LOA of 1.9 to -1.8 ms and 2.3 to -1.5; EX: meanRR bias of 11.2 to 6.0 ms; LOA of 29.8 to -7.4 ms during low intensity exercise and 8.5 to 3.5 ms during high intensity exercise). The Movesense Medical device can be used in lieu of a reference ECG for the calculation of HRV with the potential to differentiate noise from atrial fibrillation and represents a significant advance in both a HR and HRV recording device in a chest belt form factor for lab-based or remote field-application.

Reliability of Symbolic Analysis of Heart Rate Variability and Its Changes During Sympathetic Stimulation in Elite Modern Pentathlon Athletes: A Pilot Study

Background and Purpose

Most studies on heart rate variability (HRV) in professional athletes concerned linear, time-, and frequency-domain indices, and there is lack of studies on non-linear parameters in this group. The study aimed to determine the inter-day reliability, and group-related and individual changes of short-term symbolic dynamics (SymDyn) measures during sympathetic nervous system activity (SNSa) stimulation among elite modern pentathletes.

Methods

Short-term electrocardiographic recordings were performed in stable measurement conditions with a 7-day interval between tests. SNSa stimulation via isometric handgrip strength test was conducted on the second day of study. The occurrence rate of patterns without variations (0V), with one variation (1V), two like (2LV), and two unlike variations (2UV) obtained using three approaches (the Max–min, the σ, and the Equal-probability methods) were analyzed. Relative and absolute reliability were evaluated.

Results

All SymDyn indices obtained using the Max–min method, 0V, and 2UV obtained using the σ method, 2UV obtained using the Equal-probability method presented acceptable inter-day reliability (the intraclass correlation coefficient between .91 and .99, Cohen’s d between −.08 and .10, the within-subject coefficient of variation between 4% and 22%). 2LV, 2UV, and 0V obtained using the Max–min and σ methods significantly decreased and increased, respectively, during SNSa stimulation—such changes were noted for all athletes. There was no significant association between differences in SymDyn parameters and respiratory rate in stable conditions and while comparing stable conditions and SNSa stimulation.

Conclusion

SymDyn indices may be used as reliable non-respiratory-associated parameters in laboratory settings to detect autonomic nervous system (ANS) activity modulations in elite endurance athletes. These findings provide a potential solution for addressing the confounding influence of respiration frequency on HRV-derived inferences of cardiac autonomic function. For this reason, SymDyn may prove to be preferable for field-based monitoring where measurements are unsupervised.

The Effects of Providing Advance Notice and Stress-Coping Traits on Physiological Stress of Patients during Dental Treatment

Patients tend to feel stress in association with dental treatment due to uneasiness and fear. We investigated the effects of providing advance notice and stress-coping traits on the physiological stress of patients during dental treatment. Sixty non-dental professionals (male, n = 26; female, n = 34; mean age, 49.9 years) were recruited for this study and informed consent was obtained. Subjects were given simulated dental treatment including three stimuli, air, percussion and running of an air turbine, with or without advance notice of the stimulation during dental treatment. Real-time sympathetic nerve activity (SN) and parasympathetic nerve activity (PN) during the treatment were measured using a biological information monitor. The stress-coping traits of each subject were examined using the Lazarus-Type Stress Coping Inventory (SCI). Correlations between the nerve-activity and scores of eight stress-coping strategies of SCI and the presence/absence of advance notice were analyzed. Age, types of stimuli and order of stimuli significantly affected SN, while age, types of stimuli, and the pattern of stimulation significantly affected PN. The interaction of the stress-coping trait and presence/absence of advance notice significantly affected PN. Providing advance notice may have different effects on physiological stress depending on how the patient copes with stress.

Hierarchical Amplitude-Aware Permutation Entropy-Based Fault Feature Extraction Method for Rolling Bearings

In order to detect the incipient fault of rolling bearings and to effectively identify fault characteristics, based on amplitude-aware permutation entropy (AAPE), an enhanced method named hierarchical amplitude-aware permutation entropy (HAAPE) is proposed in this paper to solve complex time series in a new dynamic change analysis. Firstly, hierarchical analysis and AAPE are combined to excavate multilevel fault information, both low-frequency and high-frequency components of the abnormal bearing vibration signal. Secondly, from the experimental analysis, it is found that HAAPE is sensitive to the early failure of rolling bearings, which makes it suitable to evaluate the performance degradation of a bearing in its run-to-failure life cycle. Finally, a fault feature selection strategy based on HAAPE is put forward to select the bearing fault characteristics after the application of the least common multiple in singular value decomposition (LCM-SVD) method to the fault vibration signal. Moreover, several other entropy-based methods are also introduced for a comparative analysis of the experimental data, and the results demonstrate that HAAPE can extract fault features more effectively and with a higher accuracy.

Refined Multiscale Entropy Analysis of Wrist Pulse for Gender Difference in Traditional Chinese Medicine among Young Individuals

Pulse signal analysis plays an important role in promoting the objectification of traditional Chinese medicine (TCM). Like electrocardiogram (ECG) signals, wrist pulse signals are mainly caused by cardiac activities and are valuable in analyzing cardiac diseases. A large number of studies have reported ECG signals can distinguish gender characteristics of normal healthy subjects using entropy complexity measures, consistently showing more complexity in females than males. No research up to date, however, has been found on examining gender differences with wrist pulse signals of healthy subjects on entropy complexity measures. This paper is aimed to fill in the research gap, which could, in turn, provide a deeper insight into the pulse signal and might identify potential differences between ECG signals and pulse signals. In particular, several complementary entropy measures with corresponding refined composite multiscale versions are established to perform the analysis for the filtered TCM pulse signals. Experimental results reveal that regardless of entropy measures used, there is no statistically significant gender difference in terms of entropy complexity, indicating that the pulse signal holds less gender characteristics than the ECG signal. In view of these findings, wrist pulse signals could be likely to provide different pieces of information to ECG signals. The present study is the first to quantitatively evaluate gender differences in healthy pulse signals with measures of entropy complexity and more importantly; we expect this study could make contribution to the ongoing pulse intelligent diagnosis and objective analysis, further facilitating the modernization of TCM pulse diagnosis.

Attractor Reconstruction for Quantifying the Arterial Pulse Wave Morphology During Device-Guided Slow Breathing

Attractor reconstruction is a new way to quantify the morphology of a cardiovascular waveform by plotting all data points in the three-dimensional phase space, generating a trajectory of overlapping loops. The aim of this study is to show the feasibility of an automatic approach to quantify pulse wave attractors from a device-guided breathing study, and to link attractor features to pulse waveform features. The recently developed feature extraction technique is applied to arterial pulse waveform data of 30 patients with treated hypertension. The patients performed a device-guided slow breathing exercise. The generated attractors were categorized into three different shapes: triangular attractors, bent attractors and attractors with overlapping arms. The average height of the attractors continuously and progressively dropped from 41.8 [35.4, 55.1] AU to 34.5 [25.4, 47.3] AU (p < 0.001) during the breathing exercise. We have shown that the novel approach to quantify pulse wave attractors is feasible and can be used to detect changes in the morphology of arterial pulse waveforms. Device-guided slow breathing exercise has a shrinking effect on the average height of the attractors, which may indicate a reduction in blood pressure.

Correlation between heart rate variability and polysomnography-derived scores of obstructive sleep apnea

Obstructive sleep apnea (OSA) is one of the most common sleep disorders and affects nearly a billion people worldwide. Furthermore, it is estimated that many patients with OSA are underdiagnosed, which contributes to the development of comorbidities, such as cardiac autonomic imbalance, leading to high cardiac risk. Heart rate variability (HRV) is a non-invasive, widely used approach to evaluating neural control of the heart. This study evaluates the relationship between HRV indices and the presence and severity of OSA. We hypothesize that HRV, especially the nonlinear methods, can serve as an easy-to-collect marker for OSA early risk stratification. Polysomnography (PSG) exams of 157 patients were classified into four groups: OSA-free (N = 26), OSA-mild (N = 39), OSA-moderate (N = 37), and OSA-severe (N = 55). The electrocardiogram was extracted from the PSG recordings, and a 15-min beat-by-beat series of RR intervals were generated every hour during the first 6 h of sleep. Linear and nonlinear HRV approaches were employed to calculate 32 indices of HRV. Specifically, time- and frequency-domain, symbolic analysis, entropy measures, heart rate fragmentation, acceleration and deceleration capacities, asymmetry measures, and fractal analysis. Results with indices of sympathovagal balance provided support to reinforce previous knowledge that patients with OSA have sympathetic overactivity. Nonlinear indices showed that HRV dynamics of patients with OSA display a loss of physiologic complexity that could contribute to their higher risk of development of cardiovascular disease. Moreover, many HRV indices were found to be linked with clinical scores of PSG. Therefore, a complete set of HRV indices, especially the ones obtained by the nonlinear approaches, can bring valuable information about the presence and severity of OSA, suggesting that HRV can be helpful for in a quick diagnosis of OSA, and supporting early interventions that could potentially reduce the development of comorbidities.

On the Validity of Detrended Fluctuation Analysis at Short Scales

Detrended Fluctuation Analysis (DFA) has become a standard method to quantify the correlations and scaling properties of real-world complex time series. For a given scale ℓ of observation, DFA provides the function F(ℓ), which quantifies the fluctuations of the time series around the local trend, which is substracted (detrended). If the time series exhibits scaling properties, then F(ℓ)∼ℓα asymptotically, and the scaling exponent α is typically estimated as the slope of a linear fitting in the logF(ℓ) vs. log(ℓ) plot. In this way, α measures the strength of the correlations and characterizes the underlying dynamical system. However, in many cases, and especially in a physiological time series, the scaling behavior is different at short and long scales, resulting in logF(ℓ) vs. log(ℓ) plots with two different slopes, α1 at short scales and α2 at large scales of observation. These two exponents are usually associated with the existence of different mechanisms that work at distinct time scales acting on the underlying dynamical system. Here, however, and since the power-law behavior of F(ℓ) is asymptotic, we question the use of α1 to characterize the correlations at short scales. To this end, we show first that, even for artificial time series with perfect scaling, i.e., with a single exponent α valid for all scales, DFA provides an α1 value that systematically overestimates the true exponent α. In addition, second, when artificial time series with two different scaling exponents at short and large scales are considered, the α1 value provided by DFA not only can severely underestimate or overestimate the true short-scale exponent, but also depends on the value of the large scale exponent. This behavior should prevent the use of α1 to describe the scaling properties at short scales: if DFA is used in two time series with the same scaling behavior at short scales but very different scaling properties at large scales, very different values of α1 will be obtained, although the short scale properties are identical. These artifacts may lead to wrong interpretations when analyzing real-world time series: on the one hand, for time series with truly perfect scaling, the spurious value of α1 could lead to wrongly thinking that there exists some specific mechanism acting only at short time scales in the dynamical system. On the other hand, for time series with true different scaling at short and large scales, the incorrect α1 value would not characterize properly the short scale behavior of the dynamical system.

Autonomic Modulation in Duchenne Muscular Dystrophy During a Computer Task: A Prospective Transversal Controlled Trial Assessment by Non-linear Techniques

Introduction: Due to functional and autonomic difficulties faced by individuals with Duchenne Muscular Dystrophy (DMD), the use of assistive technology is critical to provide or facilitate functional abilities. The key objective was to investigate acute cardiac autonomic responses, by application of Heart Rate Variability (HRV), during computer tasks in subjects with DMD via techniques based on non-linear dynamics.

Method: HRV was attained via a Polar RS800CX. Then, was evaluated by Chaotic Global Techniques (CGT). Forty-five male subjects were included in the DMD group and age-matched with 45 in the healthy Typical Development (TD) control group. They were assessed for 20 min at rest sitting, and then 5 min whilst performing the maze task on a computer.

Results: Both TD and DMD subjects exhibited a significantly reduced HRV measured by chaotic global combinations when undertaking the computer maze paradigm tests. DMD subjects presented decreased HRV during rest and computer task than TD subjects.

Conclusion: While there is an impaired HRV in subjects with DMD, there remains an adaptation of the ANS during the computer tasks. The identification of autonomic impairment is critical, considering that the computer tasks in the DMD community may elevate their level of social inclusion, participation and independence.

Heart Rate Variability is an Independent Predictor of Lymph Node Metastasis in Patients with Cervical Cancer

Purpose

Heart rate variability (HRV) has been reported as a useful biomarker for prognostic factors in a variety of cancers. The purpose of this study was to explore the predictive value of preoperative HRV for lymph node metastasis (LNM) in patients with cervical cancer (CC).

Patients and Methods

A total of 77 patients with CC were included, including 18 LNM and 59 non-LNM patients. A five-minute resting electrocardiogram (ECG) was collected before surgery for the analysis of HRV time domain, frequency domain and Poincaré plot parameters (ie, SDNN, RMSSD, LF, HF, LF/HF, SD1, SD2 and SD2/SD1). Student’s t-tests and logistic regression were performed to determine the relationship between HRV and LNM.

Results

The LNM group had significantly lower SDNN, LF, and SD2 than the non-LNM group (all p < 0.05; all Cohen’s d > 0.5). Binary logistic regression analysis indicated that SDNN, LF and SD2 were still significantly associated with LNM. Specifically, for each 1 ms decrease in SDNN and SD2 and each 1 logarithmic unit decrease in ln (LF), the odds of LNM increased by 12%, 9%, and 86%, respectively (all p < 0.05).

Conclusion

These findings suggest an association between HRV and CC LNM, and HRV could be a potential noninvasive biomarker for the prediction of LNM in CC patients.

Autonomic Function in Parkinson's Disease Subjects Across Repeated Short-Term Dry Immersion: Evidence From Linear and Non-linear HRV Parameters

Several studies have shown that “dry” immersion appears as a promising method of rehabilitation for Parkinson's disease. Still, little is known about the cardiovascular reaction in “dry” immersion (DI), especially in Parkinson's disease (PD). Therefore, this study was aimed to evaluate the effect of repeated 45-min DI sessions on autonomic function in subjects with PD. The study group consisted of 20 subjects with PD [13 men, seven women, aged 51–66 years old, Hoehn & Yahr (H&Y) staged 1–3] were enrolled in the study according to inclusion and non-inclusion criteria. The DI program was comprised of seven 45-min DI sessions, applied within 25–30 days. Blood pressure (BP), heart rate (HR), and electrocardiogram (ECG) in the standard lead II were recorded at 1st, 4th, and 7th DI, before, on the 15, 30, and 40th min of DI session. Autonomic function was assessed with analysis of heart rate variability (HRV) using Kubios Standard version 2 software. Linear (time- and frequency-domain) and non-linear (correlation dimension, entropies, DFA1 and DFA2, percent of determinism, and recurrence) were computed. At baseline condition, time- and frequency-domain HRV parameters showed low variability of HR, which indicates reduced autonomic neurogenic control of HR. Throughout the DI session, systolic and diastolic BP has decreased by 5–7 mm Hg (p < 0.001), and time- and frequency-domain parameters of HRV have significantly increased, what can be regarded as compensatory mechanisms of hemodynamics during DI. The structure of the regulatory input to the heart seen by HRV was characterized by low complexity and reduced autonomic neurogenic control of HR. Across the program of DI sessions, the hypotensive effect was documented, but no notable modification of the HRV-parameters was found. The absence of long-term modification of the studied parameters can be attributed both to deconditioning environmental effect of DI and limited adaptation of the organism due to neurodegeneration in PD. That should be taken into consideration when planning rehabilitation measures in subjects of older age and chronic somatic diseases with modeled microgravity.

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.

Association of Short-Term Heart Rate Variability With Breast Tumor Stage

Cardiac autonomic modulation, assessed by heart rate variability (HRV), is associated with tumor pathogenesis and development as well as invasion and metastasis. This study aimed to examine this association in breast cancer (BC) patients. A total of 133 patients (average age 49.2years) with BC or benign breast tumors were divided into three groups: benign group, early-stage group, and advanced-stage group. About 5-min resting ECG was collected for the analysis of linear and nonlinear HRV parameters. Multiple logistic regression models were performed to test the independent contribution of HRV to breast tumor stage. The advanced-stage group had significantly reduced HRV compared to the benign and early-stage groups. In particular, for each 1-SD increase in SD2, SD of normal-to-normal intervals, very-low frequency, total power, and low frequency, the odds of having advanced staging decreased by 69.3, 64.3, 58.3, 53.3, and 65.9%, respectively. These associations were independent of age, body mass index, mean heart rate (HR), and respiratory rate (RR). These findings suggest an association between HRV and breast tumor stage, and HRV parameters may help construct an effective early diagnostic and clinical prognostic model.

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.

Aerobic Threshold Identification in a Cardiac Disease Population Based on Correlation Properties of Heart Rate Variability

An index of heart rate (HR) variability correlation properties, the short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA a1) has shown potential to delineate the first ventilatory threshold (VT1). This study aims to extend this concept to a group of participants with cardiac disease. Sixteen volunteers with stable coronary disease or heart failure performed an incremental cycling ramp to exhaustion PRE and POST a 3-week training intervention. Oxygen uptake (VO₂) and HR at VT1 were obtained from a metabolic cart. An ECG was processed for DFA a1 and HR. The HR variability threshold (HRVT) was defined as the VO₂, HR or power where DFA a1 reached a value of 0.75. Mean VT1 was reached at 16.82 ± 5.72 mL/kg/min, HR of 91.3 ± 11.9 bpm and power of 67.8 ± 17.9 watts compared to HRVT at 18.02 ± 7.74 mL/kg/min, HR of 94.7 ± 14.2 bpm and power of 73.2 ± 25.0 watts. Linear relationships were seen between modalities, with Pearson’s r of 0.95 (VO₂), 0.86 (HR) and 0.87 (power). Bland–Altman assessment showed mean differences of 1.20 mL/kg/min, 3.4 bpm and 5.4 watts. Mean peak VO₂ and VT1 did not change after training intervention. However, the correlation between PRE to POST change in VO₂ at VT1 with the change in VO₂ at HRVT was significant (r = 0.84, p < 0.001). Reaching a DFA a1 of 0.75 was associated with the VT1 in a population with cardiac disease. VT1 change after training intervention followed that of the HRVT, confirming the relationship between these parameters.

Alternations in the Cardiovascular Autonomic Regulation and Growth Factors in Autism

Autism spectrum disorder (ASD) represents a serious neurodevelopmental disorder associated with autonomic nervous system dysregulation. The aim was to study complex cardiovascular autonomic regulation using heart rate variability (HRV) and systolic blood pressure variability (SBPV) linear/non-linear analysis at rest and during orthostasis, and to assess plasma levels of epidermal growth factor (EGF) and vascular endothelial growth factor (VEGF) in autistic children. Twenty-five ASD boys and 25 age and gender-matched children at the age 7-15 years were examined. After venous blood taking, continuous ECG and blood pressure biosignals were recorded at rest and during orthostasis. Evaluated parameters: RR intervals, high- and low-frequency band of HRV spectral analysis (HF-HRV, LF-HRV), symbolic dynamics parameters 0V%, 1V%, 2LV%, 2UV%, low- and high-frequency band of SBPV (LF-SBPV, HF-SBPV), systolic, diastolic, mean blood pressure, EGF, VEGF plasma levels. RR intervals were significantly shortened and the HF-HRV, LF-SBPV, HF-SBPV parameters were significantly lower at rest, the HF-HRV and LF-SBPV remained lower during orthostasis in autistic children compared to controls (p<0.05). EGF plasma levels were significantly lower in ASD compared to controls (p=0.046). No significant differences were found in remaining parameters. Our study revealed tachycardia, cardiovagal underactivity, and blunted sympathetic vasomotor regulation at rest and during orthostasis in autistic children. Additionally, complex heart rate dynamics are similar in autistic children than controls. Furthermore, EGF was reduced in autistic children without significant correlations with any autonomic parameters. We suggest that the abnormal complex cardiovascular reflex control could contribute to understanding the pathway linking autonomic features and autism.