Sample entropy of electrocardiographic RR and QT time-series data during rest and exercise

From physiological complexity to data interactions-A case study of recordings from exercise monitoring

The popularity of nonlinear analysis has been growing simultaneously with the technology of effort monitoring. Therefore, considering the simple methods of physiological data collection and the approaches from the information domain, we proposed integrating univariate and bivariate analysis for the rest and effort comparison. Two sessions separated by an intensive training program were studied. Nine subjects participated in the first session (S1) and seven in the second session (S2). The protocol included baseline (BAS), exercise, and recovery phase. During all phases, electrocardiogram (ECG) was recorded. For the analysis, we selected corresponding data lengths of BAS and exercise usually lasting less than 5 min. We found the utility of the differences between original data and their surrogates for sample entropy Sdiff and Kullback-Leibler divergence KLDdiff. Sdiff of heart rate variability was negative in BAS and exercise but its sensitivity for phases discrimination was not satisfactory. We studied the bivariate analysis of RR intervals and corresponding QT peaks by Interlayer Mutual Information (IMI) and average edge overlap (AVO) markers. While the IMI parameter decreases in exercise conditions, AVO increased in effort compared to BAS. These findings conclude that researchers should consider a bivariate analysis of extracted RR intervals and corresponding QT datasets, when only ECG is recorded during tests.

Evaluation of traffic signs information volume at highway tunnel entrance zone based on the visual sample entropy of novice and experienced drivers

OBJECTIVE

Traffic signs play a crucial role in ensuring road traffic safety, particularly in high-risk driving zones like the entrance zone of highway tunnels. However, the impact of traffic signs information volume (TSIV) on the performance of experienced and novice drivers in this specific zone remains unclear. This study aims to investigate the influence of TSIV on the visual sample entropy (SampEn) of both experienced and novice drivers in the entrance zone of highway tunnels.

METHODS

A real vehicle driving trial was conducted with 60 participants approaching tunnels under various TSIV conditions. Fixation duration SampEn, saccade duration SampEn, and saccade amplitude SampEn were analyzed for both driver groups.

RESULTS

As both driver groups approached the tunnel entrance, fixation duration SampEn, saccade duration SampEn, and saccade amplitude SampEn gradually increased. Initially, visual SampEn declined, followed by an increase as the TSIV level increased. The lowest value of visual SampEn was observed under the T3 TSIV condition (48.31 bits). Experienced drivers exhibited lower visual SampEn compared to novice drivers under similar TSIV conditions.

CONCLUSIONS

TSIV and driving experience significantly influence the visual SampEn of drivers approaching the tunnel entrance. Optimal visual search patterns and performance were observed under the T3 TSIV condition (48.31 bits), which is critical for ensuring driving safety in the entrance zone of highway tunnels. Additionally, experienced drivers demonstrate better adaptation to tunnel environments and TSIV, while novice drivers may benefit from additional training to enhance their visual perception and performance.

Heart Rate Variability-Derived Thresholds for Exercise Intensity Prescription in Endurance Sports: A Systematic Review of Interrelations and Agreement with Different Ventilatory and Blood Lactate Thresholds

BACKGROUND

Exercise intensities are prescribed using specific intensity zones (moderate, heavy, and severe) determined by a 'lower' and a 'higher' threshold. Typically, ventilatory (VT) or blood lactate thresholds (LT), and critical power/speed concepts (CP/CS) are used. Various heart rate variability-derived thresholds (HRVTs) using different HRV indices may constitute applicable alternatives, but a systematic review of the proximity of HRVTs to established threshold concepts is lacking.

OBJECTIVE

This systematic review aims to provide an overview of studies that determined HRVTs during endurance exercise in healthy adults in comparison with a reference VT and/or LT concept.

METHODS

A systematic literature search for studies determining HRVTs in healthy individuals during endurance exercise and comparing them with VTs or LTs was conducted in Scopus, PubMed and Web of Science (until January 2022). Studies claiming to describe similar physiological boundaries to delineate moderate from heavy (HRVTlow vs. VTlow and/or LTlow), and heavy from severe intensity zone (HRVThigh vs. VThigh and/or LThigh) were grouped and their results synthesized.

RESULTS

Twenty-seven included studies (461 participants) showed a mean difference in relative HR between HRVTlow and VTlow of - 0.6%bpm in weighted means and 0.02%bpm between HRVTlow and LTlow. Bias between HR at HRVTlow and VTlow was 1 bpm (limits of agreement (LoA): - 10.9 to 12.8 bpm) and 2.7 bpm (LoA: - 20.4 to 25.8 bpm) between HRVTlow and LTlow. Mean difference in HR between HRVThigh and VThigh was 0.3%bpm in weighted means and 2.9%bpm between HRVThigh and LThigh while bias between HR at HRVThigh and VThigh was - 4 bpm (LoA: - 17.9 to 9.9 bpm) and 2.5 bpm (LoA: - 12.1 to 17.1 bpm) between HRVThigh and LThigh.

CONCLUSION

HRVTlow seems to be a promising approach for the determination of a 'lower' threshold comparable to VTlow and potentially for HRVThigh compared to VThigh, although the latter needs further empirical evaluation. LoA for both intensity zone boundaries indicates bias of HRVTs on an individual level. Taken together, HRVTs can be a promising alternative for prescribing exercise intensity in healthy, male athletes undertaking endurance activities but due to the heterogeneity of study design, threshold concepts, standardization, and lack of female participants, further research is necessary to draw more robust and nuanced conclusions.

ECG Approximate Entropy in the Elderly during Cycling Exercise

Approximate entropy (ApEn) is used as a nonlinear measure of heart-rate variability (HRV) in the analysis of ECG time-series recordings. Previous studies have reported that HRV can differentiate between frail and pre-frail people. In this study, EEGs and ECGs were recorded from 38 elderly adults while performing a three-stage cycling routine. Before and after cycling stages, 5-min resting-state EEGs (rs-EEGs) and ECGs were also recorded under the eyes-open condition. Applying the K-mean classifier to pre-exercise rs-ECG ApEn values and body weights revealed nine females with EEG power which was far higher than that of the other subjects in all cycling stages. The breathing of those females was more rapid than that of other subjects and their average heart rate was faster. Those females also presented higher degrees of asymmetry in the alpha and theta bands (stronger power levels in the right frontal electrode), indicating stressful responses during the experiment. It appears that EEG delta activity could be used in conjunction with a very low ECG frequency power as a predictor of bursts in the heart rate to facilitate the monitoring of elderly adults at risk of heart failure. A resting ECG ApEn index in conjunction with the subject's weight or BMI is recommended for screening high-risk candidates prior to exercise interventions.

Study on the nonfatigue and fatigue states of orchard workers based on electrocardiogram signal analysis

In recent years, fatigue has become an important issue in modern life that cannot be ignored, especially in some special occupations. Agricultural workers are high-risk occupations that, under fatigue conditions over a long period, will cause health problems. In China, since very few studies have focused on the fatigue state of agricultural workers, we were interested in using electrocardiogram (ECG) signals to analyze the fatigue state of agricultural workers. Healthy agricultural workers were randomly recruited from hilly orchards in South China. Through the field experiment, 130 groups of 5-min interval ECG signals were collected, and we analyzed the ECG signal by HRV. The time domain (meanHR, meanRR, SDNN, RMSSD, SDSD, PNN20, PNN50 and CV), frequency domain (VLF percent, LF percent, HF percent, LF norm, HF norm and LF/HF) and nonlinear parameters (SD1, SD2, SD1/SD2 and sample entropy) were calculated and Spearman correlation coefficient analysis and Mann-Whitney U tests were performed on each parameter for further analysis. For all subjects, nine parameters were slightly correlated in nonfatigue and fatigue state. Six parameters were significantly increased and ten HRV parameters were significantly decreased compared the nonfatigue state. As for males, fifteen parameters were significantly different, and for females, eighteen parameters were significantly different. In addition, the probability density functions of SDNN, SDSD, VLF%, HFnorm and LF/HF were significantly different in nonfatigue and fatigue state for different genders, and the nonlinear parameters become more discrete compared the nonfatigue state. Finally, we obtained the most suitable parameters, which reflect the fatigue characteristics of orchard workers under different genders. The results have instructional significance for identifying fatigue in orchard workers and provide a convincing and valid reference for clinical diagnosis.

Acute Effects of Various Movement Noise in Differential Learning of Rope Skipping on Brain and Heart Recovery Analyzed by Means of Multiscale Fuzzy Measure Entropy

In search of more detailed explanations for body-mind interactions in physical activity, neural and physiological effects, especially regarding more strenuous sports activities, increasingly attract interest. Little is known about the underlying manifold (neuro-)physiological impacts induced by different motor learning approaches. The various influences on brain or cardiac function are usually studied separately and modeled linearly. Limitations of these models have recently led to a rapidly growing application of nonlinear models. This study aimed to investigate the acute effects of various sequences of rope skipping on irregularity of the electrocardiography (ECG) and electroencephalography (EEG) signals as well as their interaction and whether these depend on different levels of active movement noise, within the framework of differential learning theory. Thirty-two males were randomly and equally distributed to one of four rope skipping conditions with similar cardiovascular but varying coordinative demand. ECG and EEG were measured simultaneously at rest before and immediately after rope skipping for 25 mins. Signal irregularity of ECG and EEG was calculated via the multiscale fuzzy measure entropy (MSFME). Statistically significant ECG and EEG brain area specific changes in MSFME were found with different pace of occurrence depending on the level of active movement noise of the particular rope skipping condition. Interaction analysis of ECG and EEG MSFME specifically revealed an involvement of the frontal, central, and parietal lobe in the interplay with the heart. In addition, the number of interaction effects indicated an inverted U-shaped trend presenting the interaction level of ECG and EEG MSFME dependent on the level of active movement noise. In summary, conducting rope skipping with varying degrees of movement variation appears to affect the irregularity of cardiac and brain signals and their interaction during the recovery phase differently. These findings provide enough incentives to foster further constructive nonlinear research in exercise-recovery relationship and to reconsider the philosophy of classical endurance training.

Longitudinal Changes and Recovery in Heart Rate Variability of Young Healthy Subjects When Exposure to a Hypobaric Hypoxic Environment

Background: The autonomic nervous system (ANS) is crucial for acclimatization. Investigating the responses of acute exposure to a hypoxic environment may provide some knowledge of the cardiopulmonary system’s adjustment mechanism.

Objective: The present study investigates the longitudinal changes and recovery in heart rate variability (HRV) in a young healthy population when exposed to a simulated plateau environment.

Methods: The study followed a strict experimental paradigm in which physiological signals were collected from 33 healthy college students (26 ± 2 years, 171 cm ± 7 cm, 64 ± 11 kg) using a medical-grade wearable device. The subjects were asked to sit in normoxic (approximately 101 kPa) and hypoxic (4,000 m above sea level, about 62 kPa) environments. The whole experimental process was divided into four stable resting measurement segments in chronological order to analyze the longitudinal changes of physical stress and recovery phases. Seventy-six time-domain, frequency-domain, and non-linear indicators characterizing rhythm variability were analyzed in the four groups.

Results: Compared to normobaric normoxia, participants in hypobaric hypoxia had significantly lower HRV time-domain metrics, such as RMSSD, MeanNN, and MedianNN (p < 0.01), substantially higher frequency domain metrics such as LF/HF ratio (p < 0.05), significantly lower Poincaré plot parameters such as SD1/SD2 ratio and other Poincaré plot parameters are reduced considerably (p < 0.01), and Refined Composite Multi-Scale Entropy (RCMSE) curves are reduced significantly (p < 0.01).

Conclusion: The present study shows that elevated heart rates, sympathetic activation, and reduced overall complexity were observed in healthy subjects exposed to a hypobaric and hypoxic environment. Moreover, the results indicated that Multiscale Entropy (MSE) analysis of RR interval series could characterize the degree of minor physiological changes. This novel index of HRV can better explain changes in the human ANS.

Linear and nonlinear analyses of normal and fatigue heart rate variability signals for miners in high-altitude and cold areas

BACKGROUND AND OBJECTIVE

Fatigue is an important cause of operational errors, and human errors are the main cause of accidents. This study is an exploratory study in China. Field tests were conducted on heart rate variability (HRV) parameters and physiological indicators of fatigue among miners in high-altitude, cold and low-oxygen areas. This paper studies heart activity patterns during work fatigue in miners.

METHODS

Fatigue affects both the sympathetic and parasympathetic nervous systems, and it is expressed as an abnormal pattern of HRV parameters. Thirty miners were selected as subjects for a field test, and HRV was extracted from 60 groups of electrocardiography (ECG) datasets as basic signals for fatigue analysis. Then, we analyzed the HRV signals of the miners using linear (time domain and frequency domain) and nonlinear dynamics (Poincaré plot and sample entropy (SampEn)), and a Pearson's correlation coefficient analysis and t-tests were performed on the measured indices.

RESULTS

The results showed that the time-domain indices (SDNN, RMSSD, SDSD, pNN50, RRn, heart rate (HR), R-wave humps (RH)) and the coefficient of variation (CV)) and the frequency-domain indices (low frequency/high frequency (LF/HF), LFnorm and HFnorm) clearly changed after fatigue. These features were selected using a Poincaré plot, sample entropy, Pearson's correlation coefficient and a t-test for further analysis. The fatigue characteristics and sensitivity parameters of miners in a high-altitude, cold and hypoxic environment were obtained.

CONCLUSIONS

This study provides deep insight into the use of linear and nonlinear fatigue characteristics to effectively and reliably identify miner fatigue. Furthermore, the study provides a reference for clinical studies of acute mountain sickness in high-altitude, cold and hypoxic environments.

Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?

It has been suggested that a viable strategy to improve complexity estimation based on the assessment of pattern similarity is to increase the pattern matching rate without enlarging the series length. We tested this hypothesis over short simulations of nonlinear deterministic and linear stochastic dynamics affected by various noise amounts. Several transformations featuring a different ability to increase the pattern matching rate were tested and compared to the usual strategy adopted in sample entropy (SampEn) computation. The approaches were applied to evaluate the complexity of short-term cardiac and vascular controls from the beat-to-beat variability of heart period (HP) and systolic arterial pressure (SAP) in 12 Parkinson disease patients and 12 age- and gender-matched healthy subjects at supine resting and during head-up tilt. Over simulations, the strategies estimated a larger complexity over nonlinear deterministic signals and a greater regularity over linear stochastic series or deterministic dynamics importantly contaminated by noise. Over short HP and SAP series the techniques did not produce any practical advantage, with an unvaried ability to discriminate groups and experimental conditions compared to the traditional SampEn. Procedures designed to artificially increase the number of matches are of no methodological and practical value when applied to assess complexity indexes.

Autonomic Control of the Heart and Its Clinical Impact. A Personal Perspective

This essay covers several aspects of the autonomic control of the heart, all relevant to cardiovascular pathophysiology with a direct impact on clinical outcomes. Ischemic heart disease, heart failure, channelopathies, and life-threatening arrhythmias are in the picture. Beginning with an overview on some of the events that marked the oscillations in the medical interest for the autonomic nervous system, our text explores specific areas, including experimental and clinical work focused on understanding the different roles of tonic and reflex sympathetic and vagal activity. The role of the baroreceptors, not just for the direct control of circulation but also because of the clinical value of interpreting alterations (spontaneous or induced) in their function, is discussed. The importance of the autonomic nervous system for gaining insights on risk stratification and for providing specific antiarrhythmic protection is also considered. Examples are the interventions to decrease sympathetic activity and/or to increase vagal activity. The non-invasive analysis of the RR and QT intervals provides additional information. The three of us have collaborated in several studies and each of us contributes with very specific and independent areas of expertise. Here, we have focused on those areas to which we have directly contributed and hence speak with personal experience. This is not an attempt to provide a neutral and general overview on the autonomic nervous system; rather, it represents our effort to share and provide the readers with our own personal views matured after many years of research in this field.

A New Physically Meaningful Threshold of Sample Entropy for Detecting Cardiovascular Diseases

Sample Entropy (SampEn) is a popular method for assessing the regularity of physiological signals. Prior to the entropy calculation, certain common parameters need to be initialized: Embedding dimension m, tolerance threshold r and time series length N. Nevertheless, the determination of these parameters is usually based on expert experience. Improper assignments of these parameters tend to bring invalid values, inconsistency and low statistical significance in entropy calculation. In this study, we proposed a new tolerance threshold with physical meaning (rp), which was based on the sampling resolution of physiological signals. Statistical significance, percentage of invalid entropy values and ROC curve were used to evaluate the proposed rp against the traditional threshold (rt). Normal sinus rhythm (NSR), congestive heart failure (CHF) as well as atrial fibrillation (AF) RR interval recordings from Physionet were used as the test data. The results demonstrated that the proposed rp had better stability than rt, hence more adaptive to detect cardiovascular diseases of CHF and AF.

Entropy Measures as Descriptors to Identify Apneas in Rheoencephalographic Signals

Rheoencephalography (REG) is a simple and inexpensive technique that intends to monitor cerebral blood flow (CBF), but its ability to reflect CBF changes has not been extensively proved. Based on the hypothesis that alterations in CBF during apnea should be reflected in REG signals under the form of increased complexity, several entropy metrics were assessed for REG analysis during apnea and resting periods in 16 healthy subjects: approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), corrected conditional entropy (CCE) and Shannon entropy (SE). To compute these entropy metrics, a set of parameters must be defined a priori, such as, for example, the embedding dimension m, and the tolerance threshold r. A thorough analysis of the effects of parameter selection in the entropy metrics was performed, looking for the values optimizing differences between apnea and baseline signals. All entropy metrics, except SE, provided higher values for apnea periods (p-values < 0.025). FuzzyEn outperformed all other metrics, providing the lowest p-value (p = 0.0001), allowing to conclude that REG signals during apnea have higher complexity than in resting periods. Those findings suggest that REG signals reflect CBF changes provoked by apneas, even though further studies are needed to confirm this hypothesis.

Short-term QT interval variability in patients with coronary artery disease and congestive heart failure: a comparison with healthy control subjects

This study aimed to test how different QT interval variability (QTV) indices change in patients with coronary artery disease (CAD) and congestive heart failure (CHF). Twenty-nine healthy volunteers, 29 age-matched CAD patients, and 20 age-matched CHF patients were studied. QT time series were derived from 5-min resting lead-II electrocardiogram (ECG). Time domain indices [mean, SD, and QT variability index (QTVI)], frequency-domain indices (LF and HF), and nonlinear indices [sample entropy (SampEn), permutation entropy (PE), and dynamical patterns] were calculated. In order to account for possible influence of heart rate (HR) on QTV, all the calculations except QTVI were repeated on HR-corrected QT time series (QTc) using three correction methods (i.e., Bazett, Fridericia, and Framingham method). Results showed that CHF patients exhibited increased mean, increased SD, increased LF and HF, decreased T-wave amplitude, increased QTVI, and decreased PE, while showed no significant changes in SampEn. Interestingly, CHF patients also showed significantly changed distribution of the dynamical patterns with less monotonously changing patterns while more fluctuated patterns. In CAD group, only QTVI was found significantly increased as compared with healthy controls. Results after HR correction were in common with those before HR correction except for QTc based on Bazett correction. Graphical abstract Fig. The framework of this paper. The arrows show the sequential analysis of the data.

Non-linear dynamics of heart rate variability during incremental cycling exercise

Within the last years complex models of cardiovascular regulation and exercise fatigue have implemented heart rate variability (HRV) as a measure of autonomic nervous system. Using detrended fluctuation analysis (DFA) to assess heart rate correlation properties, the present study examines the influence of exercise intensity on total variability and complexity in non-linear dynamics of HRV. Sixteen cyclists performed a graded exercise test on a bicycle ergometer. HRV time domain measures and fractal correlation properties were analyzed using short-term scaling exponent alpha1 of DFA. Amplitude and complexity of HRV parameters decreased significantly. DFA-alpha1 increased from rest to low exercise intensity and showed an almost linear decrease from higher intensities until exhaustion. These findings support a qualitative change in self-organized heart rate regulation from a complex autonomic control at rest and low intensities towards a breakdown of the interaction in control mechanisms with non-autonomic heart rate control dominating at high intensities.

Non-linear dynamics of cardiac autonomic activity during cycling exercise with varied cadence

In recent years, complex models of cardiac regulation have integrated heart rate variability (HRV) as a measure of the cardiac autonomic activity during exercise. Using detrended fluctuation analysis (DFA) technique, the present study examines the influence of cycling cadence and exercise duration on non-linear dynamics of HRV. Sixteen trained cyclists performed a 60-minute exercise bout at 90% of the individual anaerobic threshold on a bicycle ergometer. Cadence was changed every 10 min (90-120-60-120-60-90 rpm). Heart rate (HR) and RR-intervals were recorded continuously during exercise. HRV time domain measures (meanRR, SDNN) and correlation properties were analyzed using short-term scaling exponent alpha1 of DFA. Moreover, blood lactate (La) and rating of perceived exertion (RPE) were recorded at regular intervals at the end of condition. HR, La and RPE increased significantly at 120 rpm compared to 60 rpm. In contrast, all analyzed HRV parameters (meanRR, SDNN, DFA-alpha1) showed a significant decrease during cycling at 120 rpm compared to 60 rpm. The comparison of the first and last 10 min with the same cadence indicates a significant increase in HR and RPE, but also a significant decrease in all analyzed HRV measures. The decrease of HRV values over time and in relation to the increase in cadence indicates a decrease in the overall variability as well as a reduction in complexity of the RR-interval-fluctuations due to the increased organismic demands. Therefore, the decrease of DFA-alpha1 might be associated with a withdrawal of the organismic system aiming at the maintenance of the homeostasis under the control of the central nervous system. In this context, non-linear HRV analyses provide a more systemic view of cardiac regulation during exercise.

Linear and nonlinear parameters of heart rate variability in ischemic stroke patients

INTRODUCTION

Cardiovascular system presents cortical modulation. Post-stroke outcome can be highly influenced by autonomic nervous system disruption. Heart rate variability (HRV) analysis is a simple non-invasive method to assess sympatho-vagal balance.

OBJECTIVES

The purpose of this study was to investigate cardiac autonomic activity in ischemic stroke patients and to asses HRV nonlinear parameters beside linear ones.

METHODS

We analyzed HRV parameters in 15 right and 15 left middle cerebral artery ischemic stroke patients, in rest condition and during challenge (standing and deep breathing). Data were compared with 15 age- and sex-matched healthy controls.

RESULTS

There was an asymmetric response after autonomic stimulation tests depending on the cortical lateralization in ischemic stroke patients. In resting state, left hemisphere stroke patients presented enhanced parasympathetic control of the heart rate (higher values for RMSSD, pNN50 and HF in normalized units). Right hemisphere ischemic stroke patients displayed a reduced cardiac parasympathetic modulation during deep breathing test. Beside time and frequency domain, using short-term ECG monitoring, cardiac parasympathetic modulation can also be assessed by nonlinear parameter SD1, that presented strong positive correlation with time and frequency domain parameters RMSSD, pNN50, HFnu, while DFA α1 index presented negative correlation with the same indices and positive correlation with the LFnu and LF/HF ratio, indicating a positive association with the sympatho-vagal balance.

CONCLUSIONS

Cardiac monitoring in clinical routine using HRV analysis in order to identify autonomic imbalance may highlight cardiac dysfunctions, thus helping preventing potential cardiovascular complications, especially in right hemisphere ischemic stroke patients with sympathetic hyperactivation.

Sympathetic neural activity, metabolic parameters and cardiorespiratory fitness in obese youths

OBJECTIVE

The main objective of this cross-sectional study is to assess the cardiac autonomic neural activity in the presence of abnormally increased body weight in youths and its relationship to metabolic risk factors and cardiorespiratory fitness (CRF).

METHODS

Sixty-four overweight and obese patients, aged 9-17 years, of both sexes, stratified according to the international BMI cut-off, were enrolled. Continuous ECG was recorded during 15 min in resting conditions, and the heart rate variability (HRV) was measured in the time domain, frequency domain and for nonlinear dynamics. In addition, cardiometabolic risk factors and CRF in effort conditions were assessed.

RESULTS

Among the overweight and obese youths, no significant differences were observed regarding metabolic parameters and heart rate, although CRF was the lowest in the severely obese youths. Likewise, no significant differences were observed in HRV, independent of how it was assessed. A positive and significant relationship, independent of the degree of obesity, pubertal stage and breathing rate under resting conditions, has been observed among sympathovagal balance, insulin and the homeostatic model assessment index. Furthermore, CRF assessed by volume oxygen peak was associated with insulin levels (r = -0.273; P < 0.05), the SD of the NN interval series (r = 0.268, P < 0.05) and the long-term variation using the Poincaré plot (PS1: r = 0.275, P < 0.05; PS2: r = 0.273, P < 0.05).

CONCLUSION

The key findings of the present study were the presence of a link among fasting insulin, HRV and CRF independent of the degree of obesity, indicating the heterogeneity of obese children and adolescents.

Heart Rate Variability of Athletes Across Concussion Recovery Milestones: A Preliminary Study

OBJECTIVE

To assess heart rate variability (HRV) in athletes with concussion across three phases of recovery.

DESIGN

A prospective matched control group design included the collection of HRV and symptoms measured by the Rivermead Post-Concussion Questionnaire. These measures were taken at 3 phases of recovery [(1) symptomatic; (2). asymptomatic; and (3) one-week after return-to-play (RTP)]. The same protocol was completed by noninjured athletes.

SETTING

Interuniversity sports teams at a single institution.

PARTICIPANTS

11 athletes, across 7 sports, diagnosed with concussion, and 11 matched-athlete controls volunteered for the study.

INTERVENTION

Physician diagnosed concussion and a sitting to standing protocol for HRV monitoring.

MAIN OUTCOME MEASURES

The frequency, time, and nonlinear domains of HRV were assessed along with the absolute difference between sitting and standing for each.

RESULTS

A 2 x 3 (group x phase) repeated-measures analysis of variance revealed significant interactions for sitting High Frequency (HF) norm, sitting Low Frequency (LF) norm, the difference between sitting and standing HF norm, and difference between sitting and standing LF norm. Acutely, athletes with concussion displayed increased LF norm and decreased HF norm while sitting and a decreased change in their HF and LF norm measures between sitting and standing. A significant group effect for sample entropy when standing was detected, with the concussed group displaying decreased values compared with the matched controls.

CONCLUSIONS

Athletes with concussion displayed autonomic dysfunction in some measures of HRV that persisted beyond RTP and were related to a previous history of concussion.

Are Nonlinear Model-Free Conditional Entropy Approaches for the Assessment of Cardiac Control Complexity Superior to the Linear Model-Based One?

OBJECTIVE

We test the hypothesis that the linear model-based (MB) approach for the estimation of conditional entropy (CE) can be utilized to assess the complexity of the cardiac control in healthy individuals.

METHODS

An MB estimate of CE was tested in an experimental protocol (i.e., the graded head-up tilt) known to produce a gradual decrease of cardiac control complexity as a result of the progressive vagal withdrawal and concomitant sympathetic activation. The MB approach was compared with traditionally exploited nonlinear model-free (MF) techniques such as corrected approximate entropy, sample entropy, corrected CE, two k -nearest-neighbor CE procedures and permutation CE. Electrocardiogram was recorded in 17 healthy subjects at rest in supine position and during head-up tilt with table angles of 15°, 30°, 45°, 60°, and 75°. Heart period (HP) was derived as the temporal distance between two consecutive R-wave peaks and analysis was carried out over stationary sequences of 256 successive HPs.

RESULTS

The performance of the MB method in following the progressive decrease of HP complexity with tilt table angles was in line with those of MF approaches and the MB index was remarkably correlated with the MF ones.

CONCLUSION

The MB approach can be utilized to monitor the changes of the complexity of the cardiac control, thus speeding up dramatically the CE calculation.

SIGNIFICANCE

The remarkable performance of the MB approach challenges the notion, generally assumed in cardiac control complexity analysis based on CE, about the need of MF techniques and could allow real-time applications.

QT interval variability in body surface ECG: measurement, physiological basis, and clinical value: position statement and consensus guidance endorsed by the European Heart Rhythm Association jointly with the ESC Working Group on Cardiac Cellular Electrophysiology

This consensus guideline discusses the electrocardiographic phenomenon of beat-to-beat QT interval variability (QTV) on surface electrocardiograms. The text covers measurement principles, physiological basis, and clinical value of QTV. Technical considerations include QT interval measurement and the relation between QTV and heart rate variability. Research frontiers of QTV include understanding of QTV physiology, systematic evaluation of the link between QTV and direct measures of neural activity, modelling of the QTV dependence on the variability of other physiological variables, distinction between QTV and general T wave shape variability, and assessing of the QTV utility for guiding therapy. Increased QTV appears to be a risk marker of arrhythmic and cardiovascular death. It remains to be established whether it can guide therapy alone or in combination with other risk factors. QT interval variability has a possible role in non-invasive assessment of tonic sympathetic activity.

Repeatability of Respiratory Exchange Ratio Time Series Analysis

Currently, there are few studies on the repeatability of a time series analysis of respiratory exchange ratio (RER) under the same conditions. This repeated-measures study compared 2 trials completed under the same conditions. After an 8-hour fast, subjects (7 male and 5 female) (mean ± SD) of age 27.3 ± 3.7 years, body weight of 71.8 ± 8.4 kg, percent body fat of 16.4 ± 8.1%, and peak oxygen uptake (V[Combining Dot Above]O2peak) of 46.0 ± 5.3 ml·kg·min completed a V[Combining Dot Above]O2peak test followed 7 days later by a cycle ergometer test at 30% of ventilatory threshold (VT) and 60% of VT for 15 minutes each. These tests were repeated again 7 days later. Paired t-tests revealed no significant differences between the tests for mean RER or sample entropy (SampEn) score at both intensities. The coefficients of variance were generally similar for the mean and SampEn of the RER. The intraclass correlation coefficient (ICC) values for the mean RER at 30% of VT were 1.00 and at 60% of VT were 0.92. The ICC values for the SampEn RER at 30% of VT were 0.81 and at 60% of VT were the lowest at 0.25. Bland-Altman plots demonstrated a measure of agreement between both methods. We demonstrated that RER measurements at 30 and 60% of VT are repeatable during steady-state cycle ergometery. Future research should determine if this finding is consistent with a larger sample size and different exercise intensities.

Heart rate variability and critical flicker fusion frequency changes during and after parachute jumping in experienced skydivers

PURPOSE

The purpose of this study was (1) to further explore the heart rate dynamics and assess a potential cardiovascular risk in response to 4000 m jumps in experienced skydivers; (2) to assess whether there is an impact of such jumps on skydivers' cortical arousal or not, which may impact their decision making processes.

METHOD

18 experienced skydivers performed successive jumps from a plane at 4000 m of height. Heart rate dynamics and cortical arousal were assessed by the use of heart rate variability and Critical Flicker Fusion Frequency (CFFF), respectively.

RESULTS

CFFF did not differ between the three measurement time points (p > 0.05). Mean heart rate increased during the jump (p < 0.001) and came back to pre-jump values after the jump (p < 0.001). Percentage of the differences of successive NN intervals greater than 50 ms (pNN50) decreased during the jump (p < 0.001) and kept lower values after the jump compared to pre-jump (p < 0.05). High-frequency power (HF) did not differ during the jump (p > 0.05) but decreased after the jump compared to both pre-jump (p < 0.01) and jump (p < 0.05). Sample entropy decreased during the jump (p < 0.001) and came back to pre-jump values after the jump (p > 0.05).

CONCLUSION

These results confirm a vagal input reduction associated with a rise of the sympathetic tone during the jump and suggests that the experienced skydiver is not exposed to a high cardiovascular risk. This study also shows that environmental stresses induced by free fall could not hamper the perceptual vigilance of experienced skydivers.

Effects of acute hypoxia on heart rate variability, sample entropy and cardiorespiratory phase synchronization

BACKGROUND

Investigating the responses of autonomic nervous system (ANS) in hypoxia may provide some knowledge about the mechanism of neural control and rhythmic adjustment. The integrated cardiac and respiratory system display complicated dynamics that are affected by intrinsic feedback mechanisms controlling their interaction. To probe how the cardiac and respiratory system adjust their rhythms in different simulated altitudes, we studied heart rate variability (HRV) in frequency domain, the complexity of heartbeat series and cardiorespiratory phase synchronization (CRPS) between heartbeat intervals and respiratory cycles.

METHODS

In this study, twelve male subjects were exposed to simulated altitude of sea level, 3000 m and 4000 m in a hypobaric chamber. HRV was assessed by power spectral analysis. The complexity of heartbeat series was quantified by sample entropy (SampEn). CRPS was determined by cardiorespiratory synchrogram.

RESULTS

The power spectral HRV indices at all frequency bands depressed according to the increase of altitude. The SampEn of heartbeat series increased significantly with the altitude (P < 0.01). The duration of CRPS epochs at 3000 m was not significantly different from that at sea level. However, it was significantly longer at 4000 m (P < 0.01).

CONCLUSIONS

Our results suggest the phenomenon of CRPS exists in normal subjects when they expose to acute hypoxia. Further, the autonomic regulation has a significantly stronger influence on CRPS in acute hypoxia. The changes of CRPS and HRV parameters revealed the different regulatory mechanisms of the cardiac and respiratory system at high altitude.

Multiscale complexity analysis of the cardiac control identifies asymptomatic and symptomatic patients in long QT syndrome type 1

The study assesses complexity of the cardiac control directed to the sinus node and to ventricles in long QT syndrome type 1 (LQT1) patients with KCNQ1-A341V mutation. Complexity was assessed via refined multiscale entropy (RMSE) computed over the beat-to-beat variability series of heart period (HP) and QT interval. HP and QT interval were approximated respectively as the temporal distance between two consecutive R-wave peaks and between the R-wave apex and T-wave end. Both measures were automatically taken from 24-hour electrocardiographic Holter traces recorded during daily activities in non mutation carriers (NMCs, n = 14) and mutation carriers (MCs, n = 34) belonging to a South African LQT1 founder population. The MC group was divided into asymptomatic (ASYMP, n = 11) and symptomatic (SYMP, n = 23) patients according to the symptom severity. Analyses were carried out during daytime (DAY, from 2PM to 6PM) and nighttime (NIGHT, from 12PM to 4AM) off and on beta-adrenergic blockade (BBoff and BBon). We found that the complexity of the HP variability at short time scale was under vagal control, being significantly increased during NIGHT and BBon both in ASYMP and SYMP groups, while the complexity of both HP and QT variability at long time scales was under sympathetic control, being smaller during NIGHT and BBon in SYMP subjects. Complexity indexes at long time scales in ASYMP individuals were smaller than those in SYMP ones regardless of therapy (i.e. BBoff or BBon), thus suggesting that a reduced complexity of the sympathetic regulation is protective in ASYMP individuals. RMSE analysis of HP and QT interval variability derived from routine 24-hour electrocardiographic Holter recordings might provide additional insights into the physiology of the cardiac control and might be fruitfully exploited to improve risk stratification in LQT1 population.

A novel technique for muscle onset detection using surface EMG signals without removal of ECG artifacts

Surface electromyography (EMG) signal from trunk muscles is often contaminated by electrocardiography (ECG) artifacts. This study presents a novel method for muscle activity onset detection by processing surface EMG against ECG artifacts. The method does not require removal of ECG artifacts from raw surface EMG signals. Instead, it applies the sample entropy (SampEn) analysis to highlight EMG activity and suppress ECG artifacts in the signal complexity domain. A SampEn threshold can then be determined for detection of muscle activity. The performance of the proposed method was examined with different SampEn analysis window lengths, using a series of combinations of 'clean' experimental EMG and ECG recordings over a wide range of signal to noise ratios (SNRs) from -10 to 10 dB. For all the examined SNRs, the window length of 128 ms yielded the best performance among all the tested lengths. Compared with the conventional amplitude thresholding and integrated profile methods, the SampEn analysis based method achieved significantly better performance, demonstrated as the shortest average latency or error among the three methods (p < 0.001 for any of the examined SNRs except 10 dB).

Influence of QRS complex detection errors on entropy algorithms. Application to heart rate variability discrimination

Signal entropy measures such as approximate entropy (ApEn) and sample entropy (SampEn) are widely used in heart rate variability (HRV) analysis and biomedical research. In this article, we analyze the influence of QRS detection errors on HRV results based on signal entropy measures. Specifically, we study the influence that QRS detection errors have on the discrimination power of ApEn and SampEn using the cardiac arrhythmia suppression trial (CAST) database. The experiments assessed the discrimination capability of ApEn and SampEn under different levels of QRS detection errors. The results demonstrate that these measures are sensitive to the presence of ectopic peaks: from a successful classification rate of 100%, down to a 75% when spikes are present. The discriminating capability of the metrics degraded as the number of misdetections increased. For an error rate of 2% the segmentation failed in a 12.5% of the experiments, whereas for a 5% rate, it failed in a 25%.

Multifractal characterisation of electrocardiographic RR and QT time-series before and after progressive exercise

The scaling (fractal) characteristics of electrocardiograms (ECG) provide information complementary to traditional linear measurements (heart rate, repolarisation rate etc.) allowing them to discriminate signal changes induced pathologically or pharmacologically. Under such interventions scaling behaviour is described by multiple local scaling exponents and the signal is termed multifractal. Exercise testing is used extensively to quantify and monitor cardiorespiratory health, yet to our knowledge there has been no previous multifractal investigation of exercise-induced changes in heart rate dynamics. Ambulatory ECGs were acquired from eight healthy participants. Linear descriptive statistics and a parameterisation of multifractal singularity spectra were determined for inter-beat (RR) and intra-beat (QT) time-series before and after exercise. Multivariate analyses of both linear and multifractal measures discriminated between pre- and post-exercise periods and proportionally more significant correlations were observed between linear than between multifractal measures. Variance was more uniformly distributed over the first three principal components for multifractal measures and the two classes of measures were uncorrelated. Order and phase randomisation of the time-series indicated that both sample distribution and correlation properties contribute to multifractalilty. This exploratory study indicates the possibility of using physical exercise in conjunction with multifractal methodology as an adjunctive description of autonomically mediated modulation of heart rate.

Exercise and cardiac regulation: what can electrocardiographic time series tell us?

Electrocardiographic (ECG) monitoring allows temporal analysis of cardiac rhythm. We are usually interested in the variability of two components of the ECG: RR interval (a surrogate marker of cardiac interval) and QT interval (the duration of ventricular depolarization/repolarization). Quantification of RR rhythm, called heart rate variability (HRV) analysis, reflects the cardiac influences of the autonomic nervous system. QT variability provides insight regarding the risk of ventricular arrhythmia, and is at least partially independent of HRV. In this review, we consider the analysis of ECG time series during physical exercise. Our objectives were to show the variety of methods that can be used to characterize these time series data and to demonstrate "normal" changes in these characteristics during exercise and recovery. Attaining a comprehensive understanding of cardiac electrical conduction changes during exercise is not straightforward: analysis methods are numerous and results require careful interpretation. However, we recommend that assessment of both HRV and QT properties yields the most valuable information, the utility of which can be appreciated from the viewpoints of the athlete (e.g. providing feedback on the cardiac effects of training), the clinician (assisting in cardiovascular screening and exercise therapy evaluation) and the exercise physiologist (providing data for physiological process modelling).

Optimal parameters study for sample entropy-based atrial fibrillation organization analysis

Sample entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N. Appropriate values for m, r and N have been recommended and widely used in the literature for the application of SampEn to some physiological time series, such as heart rate, hormonal data, etc. However, no guidelines exist for the selection of that values in other cases. Therefore, an optimal parameters study should be required for the application of SampEn to not previously analyzed biomedical signals. In the present work, a thorough analysis on the optimal values for m, r and N is presented within the context of atrial fibrillation (AF) organization estimation, computed from surface electrocardiogram recordings. Recently, the evaluation of AF organization through SampEn, has revealed clinically useful information that could be used for a better treatment of this arrhythmia. The present study analyzed optimal SampEn parameter values within two different scenarios of AF organization estimation, such as the prediction of paroxysmal AF termination and the electrical cardioversion outcome in persistent AF. As a result, interesting recommendations about the selection of m, r and N, together with the relationship between N and the sampling rate (f(s)) were obtained. More precisely, (i) the proportion between N and f(s) should be higher than 1s and f(s)>or=256 Hz, (ii) overlapping between adjacent N-length windows does not improve AF organization estimation with respect to the analysis of non-overlapping windows, and (iii) values of m and r maximizing successful classification for the analyzed AF databases should be considered within a range wider than the proposed in the literature for heart rate analysis, i.e. m=1 and m=2 and r between 0.1 and 0.25 times the standard deviation of the data.

Optimized assessment of atrial fibrillation organization through suitable parameters of sample Entropy

Sample Entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N. Appropriate values for m, r and N have been recommended in some cases, such as heart rate, hormonal data, etc., but no guidelines exist for the selection of that values. Hence, an optimal parameters study should be required for the application of SampEn to not previously analyzed biomedical signals. In this work, a thorough analysis on the optimal SampEn parameter values within two different scenarios of AF organization estimation, such as the prediction of paroxysmal AF termination and the electrical cardioversion outcome in persistent AF, is presented. Results indicated that, (i) the proportion between N and the sampling rate (ƒ(s)) should be higher than one second and ƒ(s) ≥ 256 Hz, (ii) overlapping between adjacent N-length windows does not improve organization estimation and (iii) values of m and r maximizing classification should be considered within a range wider than the proposed in the literature for heart rate analysis, i. e. m = 1 and m = 2 and r between 0.1 and 0.25 times the standard deviation of the data.

Influence of bariatric surgery on indices of cardiac autonomic control

BACKGROUND

Obesity is associated with reduced heart rate variability (HRV), reflecting detrimental changes in cardiac regulation by the autonomic nervous system (ANS). Weight loss reverses this change and ANS dysfunction is thought to have a role in obesity-related cardiac pathology. Few studies have examined the influence of weight-reduction (bariatric) surgery on cardiac autonomic control. This study therefore sought to assess longitudinal changes in indices of cardiac autonomic control following two types of bariatric procedure, laparascopic gastric banding (LGB) and biliopancreatic diversion (BPD).

METHODS

Eleven morbidly obese subjects aged 47.8 +/- 7.9 years (mean+/-SD) with BMI 48.2 +/- 6.9 kg x m(-2) underwent weight-reduction surgery: five received BPD and six received LGB. Holter ECG was recorded and HRV was quantified together with a QT variability index (QTVI), a complexity index (SampEn), and a fractal (scaling) index (DFAalpha). Repeated measures ANOVA compared the indices for the two groups as a function of time (1, 6 and 12 months follow-up).

RESULTS

BMI was reduced by up to 24% (p=0.008) post-surgery despite patients remaining obese at one-year follow-up. Several indices showed prompt and persistent improvement with progressive weight loss, QTVI being the most sensitive discriminator of recovery time (F(3,216)=16.86; p<0.0005; eta(2)=0.190). Autonomic responsiveness was functionally normal throughout. The bariatric procedures induced similar changes in cardiac autonomic control, despite their differing mechanisms of action.

CONCLUSIONS

This pilot study suggests that the mechanism responsible for improving cardiac regulation following bariatric surgery might be the weight loss itself. Furthermore, post-surgery improvement in QTVI implies that weight loss reduces the risk of ventricular arrhythmic events.

Influence of the menstrual cycle on nonlinear properties of heart rate variability in young women

This study was designed to assess the changes in nonlinear properties of heart rate (HR) variability (HRV) during the menstrual cycle by means of complexity measures, including sample entropy (SampEn) and correlation dimension (CD), and explore probable physiological interpretations for them. In 16 healthy women (mean age: 23.8 +/- 2.7 yr), complexity measures along with the spectral components of HRV (sympathovagal markers) were analyzed over 1,500 R-R intervals recorded during both the follicular phase (day 11.9 +/- 1.4) and the luteal phase (day 22.0 +/- 1.4) of each woman's menstrual cycle. Simultaneously, serum ovarian hormone (estradiol-17 and progesterone) and thyroid-related hormone [free triiodothyronine, free thyroxine (T(4)), and thyroid-stimulating hormone] concentrations were measured. With regard to HRV measures, SampEn, CD, and high-frequency (HF) components decreased from the follicular phase to the luteal phase, whereas normalized low-frequency (LF) components and the LF-to-HF ratio as well as resting HR increased. In regard to hormone levels, whereas progesterone was increased, the other hormone concentrations were unchanged. Furthermore, across the menstrual cycle, both SampEn and CD were well correlated with the spectral indexes and free T(4) concentrations, and SampEn also showed significant correlations with the ratio of estradiol-17 to progesterone concentrations. These results suggest that the nonlinear properties in HRV are altered during the regular menstrual cycle and that the autonomic nervous system, ovarian hormone balance, and free T(4) may be involved in nonlinear HR control in healthy women. All of these factors may enrich the physiological meanings of complexity measures.

Linear and nonlinear characteristics of heart rate time series in obesity and during weight-reduction surgery

Obesity is associated with abnormal cardiac regulation by the autonomic nervous system (ANS), this being reversed by weight loss. Bariatric (weight-reduction) surgery can induce substantial long-term weight reductions. This study compares the acute influence on ANS control of two different types of bariatric surgery involving laparascopic and open procedures. To distinguish between the cardiac influences of surgery and obesity, we perform the same analysis for laparascopic surgery in non-obese patients. Eight morbidly obese and five non-obese patients underwent surgery. Obese patients received either laparoscopic procedures (group A: n = 5, BMI = 44.3 +/- 2.7 kg m(2)) or open procedures (group B: n = 3, BMI = 55.2 +/- 4.5 kg m(2)) and non-obese patients received a laparoscopic procedure (group C: n = 5, BMI = 30.8 +/- 5.8 kg m(-2)). Holter ECG was recorded and heart rate variability (HRV) was quantified together with measures of complexity (sample entropy) and structure (Hurst coefficient, scaling coefficient) of the heart rate data. Multifractal characteristics of heart rate data, not previously reported for obese patients, are also quantified and interpreted. Mixed model ANOVA was used to assess the magnitudes of each quantified variable, with surgical group and perioperative time as main factors. HRV measures were influenced only during anaesthesia (LFn increase: p = 0.009; HFn decrease: p = 0.033) and did not discriminate between patient groups. Multifractality was the only characteristic of heart rate data that discriminated between patient groups, being significantly (p < 0.001) greater in non-obese (group C) compared with obese patients (groups A and B, who had similar multifractal properties). Multifractality was also enhanced during anaesthesia (p = 0.028) but did not differ for other stages. We conclude that obesity per se rather than response to surgery is the cause of reduced multifractality. Reduced multifractality in obesity might reflect a diminished 'scaling' or 'collective response' across the multiple autonomic modulators of heart rate. The multifractal method appears to be a more sensitive measure of integrated cardiac autonomic function than linear methods for these patients.

Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis

BACKGROUND

Cardiac autonomic neuropathy (CAN) in diabetes has been called a "silent killer", because so few patients realize that they suffer from it, and yet its effect can be lethal. Early sub clinical detection of CAN and intervention are of prime importance for risk stratification in preventing sudden death due to silent myocardial infarction. This study presents the usefulness of heart rate variability (HRV) and complexity analyses from short term ECG recordings as a screening tool for CAN.

METHODS

A total of 17 sets of ECG recordings during supine rest were acquired from diabetic subjects with CAN (CAN+) and without CAN (CAN-) and analyzed. Poincaré plot indexes as well as traditional time and frequency, and the sample entropy (SampEn) measure were used for analyzing variability (short and long term) and complexity of HRV respectively.

RESULTS

Reduced (p > 0.05)_Poincaré plot patterns and lower (p < 0.05) SampEn values were found in CAN+ group, which could be a practical diagnostic and prognostic marker. Classification Trees methodology generated a simple decision tree for CAN+ prediction including SampEn and Poincaré plot indexes with a sensitivity reaching 100% and a specificity of 75% (percentage of agreement 88.24%).

CONCLUSION

Our results demonstrate the potential utility of SampEn (a complexity based estimator) of HRV in identifying asymptomatic CAN.