Heart rate variability: a review

Paradoxical response to an emotional task: trait characteristics and heart-rate dynamics

The present study evaluated the heart-rate dynamics of subjects reporting decreased (responders) or paradoxically increased relaxation (nonresponders) at the end of a threatening movie. Heart-rate dynamics were characterized by indices extracted through recurrence quantification analysis (RQA) and detrended fluctuation analysis (DFA). These indices were studied as a function of a few individual characteristics: hypnotizability, gender, absorption, anxiety, and the activity of the behavioral inhibition and activation systems (BIS/BAS). Results showed that (a) the subjective experience of responsiveness is associated with the activity of the behavioral inhibition system and (b) a few RQA and DFA indices are able to capture the influence of cognitive-emotional traits, including hypnotizability, on the responsiveness to the threatening task.

Invariant heart beat span versus variant heart beat intervals and its application to fetal ECG extraction

Background

The fundamental assumptions for various kinds of fetal electrocardiogram (fECG) extraction methods are not consistent with each other, which is a very important problem needed to be ascertained.

Methods

Based on two public databases, the regularity on ECG wave durations for normal sinus rhythm is investigated statistically. Taking the ascertained regularity as an assumption, a new fECG extraction algorithm is proposed, called Partial R-R interval Resampling (PRR).

Results

Both synthetic and real abdominal ECG signals are used to test the algorithm. The results indicate that the PRR algorithm has better performance over the whole R-R interval resampling based comb filtering method (RR) and linear template method (LP), which takes advantages of both LP and RR.

Conclusions

The final drawn conclusion is: (1) the proposition should be true that the individual’s heart beat span is invariable for normal sinus rhythm; (2) the proposed PRR fetal ECG extraction algorithm can estimate the maternal ECG (mECG) more accurately and stably even in the condition of large HRV, finally resulting in better fetal ECG extraction.

Heart rate variability: a tool to explore the sleeping brain?

Sleep is divided into two main sleep stages: (1) non-rapid eye movement sleep (non-REMS), characterized among others by reduced global brain activity; and (2) rapid eye movement sleep (REMS), characterized by global brain activity similar to that of wakefulness. Results of heart rate variability (HRV) analysis, which is widely used to explore autonomic modulation, have revealed higher parasympathetic tone during normal non-REMS and a shift toward sympathetic predominance during normal REMS. Moreover, HRV analysis combined with brain imaging has identified close connectivity between autonomic cardiac modulation and activity in brain areas such as the amygdala and insular cortex during REMS, but no connectivity between brain and cardiac activity during non-REMS. There is also some evidence for an association between HRV and dream intensity and emotionality. Following some technical considerations, this review addresses how brain activity during sleep contributes to changes in autonomic cardiac activity, organized into three parts: (1) the knowledge on autonomic cardiac control, (2) differences in brain and autonomic activity between non-REMS and REMS, and (3) the potential of HRV analysis to explore the sleeping brain, and the implications for psychiatric disorders.

Exercise training improves heart rate variability after methamphetamine dependency

PURPOSE

Heart rate variability (HRV) reflects a healthy autonomic nervous system and is increased with physical training. Methamphetamine dependence (MD) causes autonomic dysfunction and diminished HRV. We compared recently abstinent methamphetamine-dependent participants with age-matched, drug-free controls (DF) and also investigated whether HRV can be improved with exercise training in the methamphetamine-dependent participants.

METHODS

In 50 participants (MD = 28; DF = 22), resting heart rate (HR; R-R intervals) was recorded over 5 min while seated using a monitor affixed to a chest strap. Previously reported time domain (SDNN, RMSSD, pNN50) and frequency domain (LFnu, HFnu, LF/HF) parameters of HRV were calculated with customized software. MD were randomized to thrice-weekly exercise training (ME = 14) or equal attention without training (MC = 14) over 8 wk. Groups were compared using paired and unpaired t-tests. Statistical significance was set at P ≤ 0.05.

RESULTS

Participant characteristics were matched between groups (mean ± SD): age = 33 ± 6 yr; body mass = 82.7 ± 12 kg, body mass index = 26.8 ± 4.1 kg·min. Compared with DF, the MD group had significantly higher resting HR (P < 0.05), LFnu, and LF/HF (P < 0.001) as well as lower SDNN, RMSSD, pNN50, and HFnu (all P < 0.001). At randomization, HRV indices were similar between ME and MC groups. However, after training, the ME group significantly (all P < 0.001) increased SDNN (+14.7 ± 2.0 ms, +34%), RMSSD (+19.6 ± 4.2 ms, +63%), pNN50 (+22.6% ± 2.7%, +173%), HFnu (+14.2 ± 1.9, +60%), and decreased HR (-5.2 ± 1.1 bpm, -7%), LFnu (-9.6 ± 1.5, -16%), and LF/HF (-0.7 ± 0.3, -19%). These measures did not change from baseline in the MC group.

CONCLUSIONS

HRV, based on several conventional indices, was diminished in recently abstinent, methamphetamine-dependent individuals. Moreover, physical training yielded a marked increase in HRV, representing increased vagal modulation or improved autonomic balance.

A multi-scale feedback ratio analysis of heartbeat interval series in healthy vs. cardiac patients

The second-order difference plot, as a modified Poincaré plot, is one of the important approaches for assessing the dynamics of heart rate variability. However, corresponding quantitative analysis methods are relatively limited. Based on the second-order difference plot, we propose a novel method, called the multi-scale feedback ratio analysis, which can measure the feedback properties of heart rate fluctuations on different temporal scales. The index [R(TF([τ(1), τ(2)]) is then defined to quantify the average feedback ratio through a definite scale range. Analysis of Gaussian white, 1/f and Brownian noises show that the feedback ratios are indeed on different levels. The method is then applied to heartbeat interval series derived from healthy subjects, subjects with congestive heart failure and subjects with atrial fibrillation. Results show that, for all groups, the feedback ratios vary with increasing time scales, and gradually reach relatively stable states. The R(TF)([10,20]) values of the three groups are significantly different. Thus, R(TF)([10,20]) becomes an effective parameter for distinguishing healthy and pathologic states. In addition, RTF([10,20]) for healthy, congestive failure and atrial fibrillation subjects are close to those of the 1/f, Brownian and white noises respectively, indicating different intrinsic dynamics.

Central insulin administration improves whole-body insulin sensitivity via hypothalamus and parasympathetic outputs in men

Animal studies suggest that insulin action in the brain is involved in the regulation of peripheral insulin sensitivity. Whether this holds true in humans is unknown. Using intranasal application of insulin to the human brain, we studied the impacts of brain insulin action on whole-body insulin sensitivity and the mechanisms involved in this process. Insulin sensitivity was assessed by hyperinsulinemic-euglycemic glucose clamp before and after intranasal application of insulin and placebo in randomized order in lean and obese men. After insulin spray application in lean subjects, a higher glucose infusion rate was necessary to maintain euglycemia compared with placebo. Accordingly, clamp-derived insulin sensitivity index improved after insulin spray. In obese subjects, this insulin-sensitizing effect could not be detected. Change in the high-frequency band of heart rate variability, an estimate of parasympathetic output, correlated positively with change in whole-body insulin sensitivity after intranasal insulin. Improvement in whole-body insulin sensitivity correlated with the change in hypothalamic activity as assessed by functional magnetic resonance imaging. Intranasal insulin improves peripheral insulin sensitivity in lean but not in obese men. Furthermore, brain-derived peripheral insulin sensitization is associated with hypothalamic activity and parasympathetic outputs. Thus, the findings provide novel insights into the regulation of insulin sensitivity and the pathogenesis of insulin resistance in humans.

Cardiac acceleration at the onset of exercise: a potential parameter for monitoring progress during physical training in sports and rehabilitation

There is a need for easy-to-use methods to assess training progress in sports and rehabilitation research. The present review investigated whether cardiac acceleration at the onset of physical exercise (HRonset) can be used as a monitoring variable. The digital databases of Scopus and PubMed were searched to retrieve studies investigating HRonset. In total 652 studies were retrieved. These articles were then classified as having emphasis on HRonset in a sports or rehabilitation setting, which resulted in 8 of 112 studies with a sports application and 6 of 68 studies with a rehabilitation application that met inclusion criteria. Two co-existing mechanisms underlie HRonset: feedforward (central command) and feedback (mechanoreflex, metaboreflex, baroreflex) control. A number of studies investigated HRonset during the first few seconds of exercise (HRonsetshort), in which central command and the mechanoreflex determine vagal withdrawal, the major mechanism by which heart rate (HR) increases. In subsequent sports and rehabilitation studies, interest focused on HRonset during dynamic exercise over a longer period of time (HRonsetlong). Central command, mechanoreflexes, baroreflexes, and possibly metaboreflexes contribute to HRonset during the first seconds and minutes of exercise, which in turn leads to further vagal withdrawal and an increase in sympathetic activity. HRonset has been described as the increase in HR compared with resting state (delta HR) or by exponential modeling, with measurement intervals ranging from 0-4 s up to 2 min. Delta HR was used to evaluate HRonsetshort over the first 4 s of exercise, as well as for analyzing HRonsetlong. In exponential modeling, the HR response to dynamic exercise is biphasic, consisting of fast (parasympathetic, 0-10 s) and slow (sympathetic, 1-4 min) components. Although available studies differed largely in measurement protocols, cross-sectional and longitudinal training studies showed that studies analyzing HRonset in relation to physical training primarily incorporated HRonsetlong. HRonsetlong slowed in athletes as well as in patients with a coronary disease, who have a relatively fast HRonsetlong. It is advised to include both HRonsetlong and HRonsetshort in further studies. The findings of this review suggest that HRonset is a potential tool for monitoring and titrating training in sports as well as in rehabilitation settings, particularly in patients with ventricular fibrillation. Monitoring HRonset in the early phase of training can help optimize the effectiveness of training and therapy. More research is needed to gain a better understanding of the mechanisms underlying HRonset in relation to their application in sports and rehabilitation settings.

Cardiovascular function and predictors of exercise capacity in patients with colorectal cancer

BACKGROUND

Patients with colorectal cancer (CRC) often present with dyspnea and fatigue. These are also frequent symptoms in patients with chronic heart failure (CHF).

OBJECTIVES

We hypothesized that similar patterns of cardiovascular perturbations are present in CRC and CHF.

METHODS

We prospectively studied 50 patients with CRC, 51 patients with CHF, and 51 control subjects. The CRC group was divided into 2 subgroups: patients who underwent chemotherapy (n = 26) and chemotherapy-naive patients (n = 24). We assessed exercise capacity (spiroergometry), cardiac function (echocardiography), heart rate variability (Holter electrocardiography), body composition (dual-energy x-ray absorptiometry), and blood parameters.

RESULTS

Compared with the control arm, the left ventricular ejection fraction (CRC group 59.4%; control group 62.5%) and exercise performance as assessed by peak oxygen consumption (peak VO2) (CRC group 21.8 ml/kg/min; control group 28.0 ml/kg/min) were significantly reduced in CRC patients (both p < 0.02). Markers of heart rate variability were markedly impaired in CRC patients compared with control subjects (all p < 0.008). Compared with the control group, the CRC group also showed reduced lean mass in the legs and higher levels of the endothelium-derived C-terminal-pro-endothelin-1 (both p < 0.02). Major determinants of cardiovascular function were impaired in chemotherapy-treated patients and in the chemotherapy-naive patients, particularly with regard to exercise capacity, left ventricular ejection fraction, lean mass, and heart rate variability (all p < 0.05 vs. control subjects).

CONCLUSIONS

Some aspects of cardiovascular function are impaired in patients with CRC. More importantly, our findings were evident independently of whether patients were undergoing chemotherapy.

Improvement of heart rate variability after decreased insulin resistance after sleeve gastrectomy for morbidly obesity patients

BACKGROUND

Morbidly obese patients display both an autonomic nervous imbalance and impaired glucose metabolism, and both of these conditions can be partially reversed after bariatric surgery. The aim of the present study was to investigate changes in heart rate variability (HRV) and glucose metabolism in patients after laparoscopic sleeve gastrectomy (SG).

METHODS

Eighteen morbidly obese patients who underwent SG were examined before surgery and at 7, 30, 90, and 180 days after surgery. Indices of HRV included time-domain, frequency-domain, and nonlinear parameters. Glucose metabolism was evaluated by the measuring levels of insulin resistance, glycated hemoglobin (HbA(1c)), and gut hormones.

RESULTS

The study included 9 men and 9 women with a mean age of 34 years. In the HRV study, the average R-R interval, median R-R interval, standard deviation of the R-R intervals, root mean squared successive difference of the R-R intervals (RMSSD), and the number of pairs of successive normal-to-normal beat intervals that differed by>50 ms significantly increased at 180 days after surgery. Regarding the frequency-domain indices, the low frequency (LF)/high frequency (HF) ratio was more balanced at 90 days after SG compared with baseline, and increases in the total power, LF band, and HF band were observed at 180 days. The assessments of insulin resistance, glucose metabolism, and gut hormones revealed not only improvements in the homeostasis model assessment of insulin resistance and HbA1c levels but also increases in the levels of glucagon-like peptide-1 at 90 and 180 days after surgery compared with baseline. A multivariable regression model revealed significantly negative associations between the perioperative changes in HOMA-IR and changes in both the RMSSD and HF band.

CONCLUSIONS

SG leads to early improvements in insulin resistance and glucose metabolism that are followed by improvements in HRV indices. Improvements in insulin resistance were associated with increases in the RMSSD and HF band index, but the mechanism of these changes require further study.

An evaluation of heart rate variability and its modifying factors in children with type 1 diabetes

OBJECTIVE

To evaluate heart rate variability by Holter monitoring in type 1 diabetic children compared with a healthy control group and determine the factors modifying heart rate variability.

METHODS

This was designed as a prospective study comparing 28 patients, diagnosed with type 1 diabetes and under follow-up, with 27 healthy control group subjects.

RESULTS

The patients were aged 9.9 ± 4.2 years in the diabetic group, including 13 (46.5%) girls and 15 (53.5%) boys. The healthy control group comprised 20 (74%) girls and seven boys (26%) with an average age of 8.6 ± 3.7 years. The search for factors modifying heart rate variability yielded the following correlations: for the time-dependent variables, negative between age and both average and maximal heart rate (r = -0.263 and -0.460, respectively), negative between haemoglobin A1c and percentage of differences between adjacent RR intervals >50 ms, positive between diabetes duration and square root of the mean of the sum of squares of differences between adjacent NN intervals. The average heart rate and percentage of differences between adjacent RR intervals >50 ms was significantly higher in the girls than the boys in all groups. With regard to the frequency-dependent factors affecting heart rate variability, correlations were found between haemoglobin A1c level and both total power and very low frequency (r = -0.751 and -0.644) and between very low frequency and diabetes duration.

CONCLUSION

A reduction in heart rate variability parameters was observed in type 1 diabetes mellitus patients who had a long disease duration or were poorly controlled, as compared with healthy controls.

From beat rate variability in induced pluripotent stem cell-derived pacemaker cells to heart rate variability in human subjects

BACKGROUND

We previously reported that induced pluripotent stem cell-derived cardiomyocytes manifest beat rate variability (BRV) resembling heart rate variability (HRV) in the human sinoatrial node. We now hypothesized the BRV-HRV continuum originates in pacemaker cells.

OBJECTIVE

To investigate whether cellular BRV is a source of HRV dynamics, we hypothesized 3 levels of interaction among different cardiomyocyte entities: (1) single pacemaker cells, (2) networks of electrically coupled pacemaker cells, and (3) the in situ sinoatrial node.

METHODS

We measured BRV/HRV properties in single pacemaker cells, induced pluripotent stem cell-derived contracting embryoid bodies (EBs), and electrocardiograms from the same individual.

RESULTS

Pronounced BRV/HRV was present at all 3 levels. The coefficient of variance of interbeat intervals and Poincaré plot indices SD1 and SD2 for single cells were 20 times greater than those for EBs (P < .05) and the in situ heart (the latter two were similar; P > .05). We also compared BRV magnitude among single cells, small EBs (~5-10 cells), and larger EBs (>10 cells): BRV indices progressively increased with the decrease in the cell number (P < .05). Disrupting intracellular Ca(2+) handling markedly augmented BRV magnitude, revealing a unique bimodal firing pattern, suggesting that intracellular mechanisms contribute to BRV/HRV and the fractal behavior of heart rhythm.

CONCLUSION

The decreased BRV magnitude in transitioning from the single cell to the EB suggests that the HRV of in situ hearts originates from the summation and integration of multiple cell-based oscillators. Hence, complex interactions among multiple pacemaker cells and intracellular Ca(2+) handling determine HRV in humans and cardiomyocyte networks.

Leptin, adiponectin, and heart rate variability among police officers

OBJECTIVES

Police officers have a high prevalence of cardiovascular disease (CVD). Reduced heart rate variability (HRV) is known to increase CVD risk. Leptin and adiponectin may be related to CVD health. Therefore, our objective was to investigate the relationship between these variables and HRV.

METHODS

Leptin and adiponectin levels were measured in 388 officers from the Buffalo Cardio-Metabolic Occupational Police Stress study. HRV was assessed according to methods published by the Task Force of the European Society of Cardiology and the North American Society of Pacing Electrophysiology for measurement and analysis of HRV. Mean values of high-frequency (HF) and low-frequency (LF) HRV were compared across tertiles of leptin and adiponectin using analysis of variance and analysis of covariance; trends were assessed using linear regression models.

RESULTS

Leptin, but not adiponectin, was significantly and inversely associated with HRV. Body mass index (BMI) and percent body fat significantly modified the association between leptin and LF (but not HF) HRV. Among officers with BMI < 25 kg/m(2) , leptin was not significantly associated with HRV. However, among officers with BMI ≥ 25 kg/m(2) , leptin was inversely associated with HRV, after adjustment for age, gender, and race/ethnicity; HF HRV, P = 0.019 and LF HRV, P < 0.0001. Similarly, among officers with percent body fat ≥ 25.5%, leptin and LF HRV showed significant, inverse associations (adjusted P = 0.001).

CONCLUSIONS

Leptin levels were inversely associated with LF HRV, especially among officers with increased adiposity. Increased leptin levels may be associated with CVD-related health problems.

Inference of human affective states from psychophysiological measurements extracted under ecologically valid conditions

Compared to standard laboratory protocols, the measurement of psychophysiological signals in real world experiments poses technical and methodological challenges due to external factors that cannot be directly controlled. To address this problem, we propose a hybrid approach based on an immersive and human accessible space called the eXperience Induction Machine (XIM), that incorporates the advantages of a laboratory within a life-like setting. The XIM integrates unobtrusive wearable sensors for the acquisition of psychophysiological signals suitable for ambulatory emotion research. In this paper, we present results from two different studies conducted to validate the XIM as a general-purpose sensing infrastructure for the study of human affective states under ecologically valid conditions. In the first investigation, we recorded and classified signals from subjects exposed to pictorial stimuli corresponding to a range of arousal levels, while they were free to walk and gesticulate. In the second study, we designed an experiment that follows the classical conditioning paradigm, a well-known procedure in the behavioral sciences, with the additional feature that participants were free to move in the physical space, as opposed to similar studies measuring physiological signals in constrained laboratory settings. Our results indicate that, by using our sensing infrastructure, it is indeed possible to infer human event-elicited affective states through measurements of psychophysiological signals under ecological conditions.

How to Calculate Renyi Entropy from Heart Rate Variability, and Why it Matters for Detecting Cardiac Autonomic Neuropathy

Cardiac autonomic neuropathy (CAN) is a disease that involves nerve damage leading to an abnormal control of heart rate. An open question is to what extent this condition is detectable from heart rate variability (HRV), which provides information only on successive intervals between heart beats, yet is non-invasive and easy to obtain from a three-lead ECG recording. A variety of measures may be extracted from HRV, including time domain, frequency domain, and more complex non-linear measures. Among the latter, Renyi entropy has been proposed as a suitable measure that can be used to discriminate CAN from controls. However, all entropy methods require estimation of probabilities, and there are a number of ways in which this estimation can be made. In this work, we calculate Renyi entropy using several variations of the histogram method and a density method based on sequences of RR intervals. In all, we calculate Renyi entropy using nine methods and compare their effectiveness in separating the different classes of participants. We found that the histogram method using single RR intervals yields an entropy measure that is either incapable of discriminating CAN from controls, or that it provides little information that could not be gained from the SD of the RR intervals. In contrast, probabilities calculated using a density method based on sequences of RR intervals yield an entropy measure that provides good separation between groups of participants and provides information not available from the SD. The main contribution of this work is that different approaches to calculating probability may affect the success of detecting disease. Our results bring new clarity to the methods used to calculate the Renyi entropy in general, and in particular, to the successful detection of CAN.

Classification of heart rate signals of healthy and pathological subjects using threshold based symbolic entropy

The dynamical fluctuations of biological signals provide a unique window to construe the underlying mechanism of the biological systems in health and disease. Recent research evidences suggest that a wide class of diseases appear to degrade the biological complexity and adaptive capacity of the system. Heart rate signals are one of the most important biological signals that have widely been investigated during the last two and half decades. Recent studies suggested that heart rate signals fluctuate in a complex manner. Various entropy based complexity analysis measures have been developed for quantifying the valuable information that may be helpful for clinical monitoring and for early intervention. This study is focused on determining HRV dynamics to distinguish healthy subjects from patients with certain cardiac problems using symbolic time series analysis technique. For that purpose, we have employed recently developed threshold based symbolic entropy to cardiac inter-beat interval time series of healthy, congestive heart failure and atrial fibrillation subjects. Normalized Corrected Shannon Entropy (NCSE) was used to quantify the dynamics of heart rate signals by continuously varying threshold values. A rule based classifier was implemented for classification of different groups by selecting threshold values for the optimal separation. The findings indicated that there is reduction in the complexity of pathological subjects as compared to healthy ones at wide range of threshold values. The results also demonstrated that complexity decreased with disease severity.

Estimation of instantaneous complex dynamics through Lyapunov exponents: a study on heartbeat dynamics

Measures of nonlinearity and complexity, and in particular the study of Lyapunov exponents, have been increasingly used to characterize dynamical properties of a wide range of biological nonlinear systems, including cardiovascular control. In this work, we present a novel methodology able to effectively estimate the Lyapunov spectrum of a series of stochastic events in an instantaneous fashion. The paradigm relies on a novel point-process high-order nonlinear model of the event series dynamics. The long-term information is taken into account by expanding the linear, quadratic, and cubic Wiener-Volterra kernels with the orthonormal Laguerre basis functions. Applications to synthetic data such as the Hénon map and Rössler attractor, as well as two experimental heartbeat interval datasets (i.e., healthy subjects undergoing postural changes and patients with severe cardiac heart failure), focus on estimation and tracking of the Instantaneous Dominant Lyapunov Exponent (IDLE). The novel cardiovascular assessment demonstrates that our method is able to effectively and instantaneously track the nonlinear autonomic control dynamics, allowing for complexity variability estimations.

[Heart rate variability is significantly reduced in non-diabetic patients with hypertension]

INTRODUCTIONS

Heart rate variability is reduced among patients with hypertension and/or with diabetes mellitus. Hypertension and diabetes show frequent co-morbidity, but it is still not entirely clear whether heart rate variability is reduced in non-diabetic patients with hypertension.

AIM

The aim of the authors was to evaluate the heart rate variability in hypertensive patients with and without diabetes and in control subjects.

METHOD

130 patients with hypertension, 48 patients with hypertension and type 2 diabetes mellitus, and 87 control subjects were involved in the study. Minimum, mean and maximum heart rate, and parameters of heart rate variability were measured.

RESULTS

The mean of minimum heart rate did not differ significantly between the three groups. However, all other parameters were significantly reduced in patients with hypertension with and without diabetes as compared to the control group. No significant differences were observed between hypertensive patients with and without diabetes mellitus.

CONCLUSIONS

Heart rate variability is significantly reduced in non-diabetic patients with hypertension. It seems that type 2 diabetes results in no further significant reduction of heart rate variability in patients with hypertension.

The interrelations among stochastic pacing, stability, and memory in the heart

Low pacing variability in the heart has been clinically reported as a risk factor for lethal cardiac arrhythmias and arrhythmic death. In ia previous simulation study, we demonstrated that stochastic pacing sustains an antiarrhythmic effect by moderating the slope of the action potential duration (APD) restitution curve, by reducing the propensity of APD alternans, converting discordant to concordant alternans, and ultimately preventing wavebreaks. However, the dynamic mechanisms relating pacing stochasticity to tissue stability are not yet known. In this work, we develop a mathematical framework to describe the APD signal using an autoregressive stochastic model, and we establish the interrelations between stochastic pacing, cardiac memory, and cardiac stability, as manifested by the degree of APD alternans. Employing stability analysis tools, we show that increased stochasticity in the ventricular tissue activation sequence works to lower the maximal absolute eigenvalues of the stochastic model, thereby contributing to increased stability. We also show that the memory coefficients of the autoregressive model are modulated by pacing stochasticity in a nonlinear, biphasic way, so that for exceedingly high levels of pacing stochasticity, the antiarrhythmic effect is hampered by increasing APD variance. This work may contribute to establishment of an optimal antiarrhythmic pacing protocol in a future study.

Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability

This study aims to develop an automatic classifier for risk assessment in patients suffering from congestive heart failure (CHF). The proposed classifier separates lower risk patients from higher risk ones, using standard long-term heart rate variability (HRV) measures. Patients are labeled as lower or higher risk according to the New York Heart Association classification (NYHA). A retrospective analysis on two public Holter databases was performed, analyzing the data of 12 patients suffering from mild CHF (NYHA I and II), labeled as lower risk, and 32 suffering from severe CHF (NYHA III and IV), labeled as higher risk. Only patients with a fraction of total heartbeats intervals (RR) classified as normal-to-normal (NN) intervals (NN/RR) higher than 80% were selected as eligible in order to have a satisfactory signal quality. Classification and regression tree (CART) was employed to develop the classifiers. A total of 30 higher risk and 11 lower risk patients were included in the analysis. The proposed classification trees achieved a sensitivity and a specificity rate of 93.3% and 63.6%, respectively, in identifying higher risk patients. Finally, the rules obtained by CART are comprehensible and consistent with the consensus showed by previous studies that depressed HRV is a useful tool for risk assessment in patients suffering from CHF.

Altered heart rate variability depend on the characteristics of coronary lesions in stable angina pectoris

OBJECTIVE

Dysfunction of cardiac autonomic nerve system is considered as one of risk factors for coronary atherosclerotic heart disease. Heart rate variability (HRV) has been used to study the correlation between damage of coronary artery and dysfunction of autonomic nervous system. We hypothesize the correlation between damage of coronary artery and dysfunction of autonomic nervous system by HRV among subjects with stable angina.

METHODS

A 236 subjects who diagnosed as stable angina pectoris by elective coronary angiography, were divided into two groups by Gensini score system (GS):GS≤32 (GS1) and GS2>32 (GS2). Subgroups were divided based on location of stenosis lesions and the number of coronary artery disease. 86 subjects suspicious with stable angina pectoris with normal coronary angiography were selected as the control group. All subjects were received 24-hour ambulatory electrocardiogram and the result of time-domain HRV was analyzed (SDNN, SDANN, SDNNind, RMSSD, PNN50).

RESULTS

Compared with control group, SDNN, SDNNind and RMSSD lower in GS1, and SDNN, SDANN, SDNNind, RMSSD, PNN50 lower in GS2; ccompared with GS1, SDNN was lower in GS2. Compared with control group, SDNN in one-vessel, SDNN, SDANN in two-vessel diseased and in three-vessel diseased were lower, and compared with two-vessel diseased, SDNN, SDANN lower in three-vessel diseased. Compared with right-coronary artery diseased, SDNN and SDANN in left-coronary artery diseased group were lower, while compared with lesions in left circumflex, SDNN in lesions in left anterior descending artery lower.

CONCLUSION

HRV may be play a crucial role in estimating the correlation between damage of coronary artery and dysfunction of autonomic nerve system.

Arrhythmia risk assessment using heart rate variability parameters in patients with frequent ventricular ectopic beats without structural heart disease

BACKGROUND

Ventricular ectopic beats (VEBs) are usually considered a benign condition that can be managed with conservative measures. Heart rate variability (HRV), which is one of the most important methods for assessing autonomic activity, is a noninvasive, quantitative method of analyzing autonomic effects on the heart. We aimed to investigate the risk of arrhythmia in patients with VEBs and without cardiovascular disease by using HRV parameters.

METHODS

Patients with frequent VEBs (more than 30 times in 1 hour, according to the Lown classification) were identified. Identified patients were evaluated by 24-hour ECG recording. Our study included 43 patients with frequent VEBs and 43 controls.

RESULTS

General characteristics of the study population were similar. The LF (low frequency)/HF (high frequency) ratio was significantly higher in the frequent VEBs group than in the control group (P < 0.001). The rate of paroxysmal atrial fibrillation (PAF) was higher in the frequent VEB group than in the control group (P = 0.003). The number of VEBs was correlated with LF/HF ratio and PAF (r = 0.339, P = 0.001 and r = 0.294, P = 0.006, respectively).

CONCLUSIONS

Our study showed that the sympathetic nervous system is dominant in young patients with VEBs and without significant comorbidities. There is a higher risk of atrial fibrillation in patients with VEBs and they should be monitored closely for atrial fibrillation.

Influence of exercise modality on agreement between gas exchange and heart rate variability thresholds

The main purpose of this study was to investigate the level of agreement between the gas exchange threshold (GET) and heart rate variability threshold (HRVT) during maximal cardiopulmonary exercise testing (CPET) using three different exercise modalities. A further aim was to establish whether there was a 1:1 relationship between the percentage heart rate reserve (%HRR) and percentage oxygen uptake reserve (%VO2 R) at intensities corresponding to GET and HRVT. Sixteen apparently healthy men 17 to 28 years of age performed three maximal CPETs (cycling, walking, and running). Mean heart rate and VO2 at GET and HRVT were 16 bpm (P<0.001) and 5.2 mL · kg(-1) · min(-1) (P=0.001) higher in running than cycling, but no significant differences were observed between running and walking, or cycling and walking (P>0.05). There was a strong relationship between GET and HRVT, with R2 ranging from 0.69 to 0.90. A 1:1 relationship between %HRR and % VO2 R was not observed at GET and HRVT. The %HRR was higher during cycling (GET mean difference=7%; HRVT mean difference=11%; both P<0.001), walking (GET mean difference=13%; HRVT mean difference=13%; both P<0.001), or running (GET mean difference=11%; HRVT mean difference=10%; both P<0.001). Therefore, using HRVT to prescribe aerobic exercise intensity appears to be valid. However, to assume a 1:1 relationship between %HRR and % VO2 R at HRVT would probably result in overestimation of the energy expenditure during the bout of exercise.

Streaming updates for heart rate variability algorithms

Heart rate variability (HRV) quantifies the fluctuations of the lengths of consecutive heart beat intervals, and is a reliable descriptor of many physiological factors modulating the normal rhythm of the heart. As the heart rate signal is nonstationary, indicators deduced from it may be present at all times, but may also occur episodically at nonpredetermined time instances. The potential for real-time feedback long-term ambulatory recordings is thus apparent. Numerous methods for measuring HRV have been standardized and are in active use, but are typically not designed to operate at real time. In this paper, we study the most popular HRV quantification methods and propose streaming algorithms that maximally utilize previously computed information without altering the output of the methods. We demonstrate speedups of more than two orders of magnitude for typical use-case scenarios. Using our algorithms on embedded systems that compute HRV leads to dramatic decreases in power consumption and in some cases allows for computation of metrics that were not previously possible at real time.

Chaotic analysis of the electroretinographic signal for diagnosis

Electroretinogram (ERG) is a time-varying potential which arises from different layers of retina. To be specific, all the physiological signals may contain some useful information which is not visible to our naked eye. However this subtle information is difficult to monitor directly. Therefore the ERG signal features which are extracted and analyzed using computers are highly useful for diagnosis. This work discusses the chaotic aspect of the ERG signal for the controls, congenital stationary night blindness (CSNB), and cone-rod dystrophy (CRD) classes. In this work, nonlinear parameters like Hurst exponent (HE), the largest Lyapunov exponent (LLE), Higuchi's fractal dimension (HFD), and approximate entropy (ApEn) are analyzed for the three different classes. It is found that the measures like HE dimension and ApEn are higher for controls as compared to the other two classes. But LLE shows no distinguishable variation for the three cases. We have also analyzed the recurrence plots and phase-space plots which shows a drastic variation among the three groups. The results obtained show that the ERG signal is highly complex for the control groups and less complex for the abnormal classes with P value less than 0.05.

Investigation into the effects of varying frequency of mechanical stimulation in a cycle-by-cycle manner on engineered cardiac construct function

Mechanical stimulation has been used extensively to improve the function of cardiac engineered tissue, as it mimics the physical environment in which the tissue is situated during normal development. However, previous mechanical stimulation has been carried out under a constant frequency that more closely resembles a diseased heart. The goal of this study was to create a bioreactor system that would allow us to control the mechanical stimulation of engineered cardiac tissue on a cycle-by-cycle basis. This unique system allows us to determine the effects on cardiac construct function of introducing variability to the mechanical stretch. To test our bioreactor system, constructs created from neonatal rat cardiomyocytes entrapped in fibrin hydrogels were stimulated under various regimes for 2 weeks and then assessed for functional outcomes. No differences were observed in the final cell number in each condition, indicating that variability in frequency did not have a negative effect on viability. The forces were higher for all mechanical stimulation groups compared to static controls, although no differences were observed between the mechanically stimulated conditions, indicating that variable frequency on a cycle-by-cycle basis has limited effects on the resulting force. Although differences in the observed twitch force were not observed, differences in the protein expression indicate that variable-frequency mechanical stimulation had an effect on cell-cell coupling and growth pathway activation in the constructs. Thus, this bioreactor system provides a valuable tool for further development and optimization of engineered myocardial tissue as a repair or replacement strategy for patients undergoing heart failure. Copyright © 2014 John Wiley & Sons, Ltd.

Revealing real-time emotional responses: a personalized assessment based on heartbeat dynamics

Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis.

Selection of entropy-measure parameters for knowledge discovery in heart rate variability data

Background

Heart rate variability is the variation of the time interval between consecutive heartbeats. Entropy is a commonly used tool to describe the regularity of data sets. Entropy functions are defined using multiple parameters, the selection of which is controversial and depends on the intended purpose. This study describes the results of tests conducted to support parameter selection, towards the goal of enabling further biomarker discovery.

Methods

This study deals with approximate, sample, fuzzy, and fuzzy measure entropies. All data were obtained from PhysioNet, a free-access, on-line archive of physiological signals, and represent various medical conditions. Five tests were defined and conducted to examine the influence of: varying the threshold value r (as multiples of the sample standard deviation σ, or the entropy-maximizing r_Chon), the data length N, the weighting factors n for fuzzy and fuzzy measure entropies, and the thresholds r_F and r_L for fuzzy measure entropy. The results were tested for normality using Lilliefors' composite goodness-of-fit test. Consequently, the p-value was calculated with either a two sample t-test or a Wilcoxon rank sum test.

Results

The first test shows a cross-over of entropy values with regard to a change of r. Thus, a clear statement that a higher entropy corresponds to a high irregularity is not possible, but is rather an indicator of differences in regularity. N should be at least 200 data points for r = 0.2 σ and should even exceed a length of 1000 for r = r_Chon. The results for the weighting parameters n for the fuzzy membership function show different behavior when coupled with different r values, therefore the weighting parameters have been chosen independently for the different threshold values. The tests concerning r_F and r_L showed that there is no optimal choice, but r = r_F = r_L is reasonable with r = r_Chon or r = 0.2σ.

Conclusions

Some of the tests showed a dependency of the test significance on the data at hand. Nevertheless, as the medical conditions are unknown beforehand, compromises had to be made. Optimal parameter combinations are suggested for the methods considered. Yet, due to the high number of potential parameter combinations, further investigations of entropy for heart rate variability data will be necessary.

The relationship between nature-based tourism and autonomic nervous system function among older adults

BACKGROUND

Nature-based tourism has recently become a topic of interest in health research. This study was aimed at examining relationships among nature-based tourism, stress, and the function of the autonomic nervous system (ANS).

METHODS

Three hundred and twenty-two older adults living in Taichung City, Taiwan, were selected as participants. Data were collected by a face-to-face survey that included measures of the frequency of participation in domestic and international nature-based tourism and the stress and ANS function of these participants. The data were analyzed using a path analysis.

RESULTS

The results demonstrated that the frequency of participation in domestic nature-based tourism directly contributed to ANS function and that it also indirectly contributed to ANS function through stress reduction.

CONCLUSIONS

Domestic nature-based tourism can directly and indirectly contribute to ANS function among older adults. Increasing the frequency of participation in domestic nature-based tourism should be considered a critical element of health programs for older adults.

Inhomogeneous point-process entropy: an instantaneous measure of complexity in discrete systems

Measures of entropy have been widely used to characterize complexity, particularly in physiological dynamical systems modeled in discrete time. Current approaches associate these measures to finite single values within an observation window, thus not being able to characterize the system evolution at each moment in time. Here, we propose a new definition of approximate and sample entropy based on the inhomogeneous point-process theory. The discrete time series is modeled through probability density functions, which characterize and predict the time until the next event occurs as a function of the past history. Laguerre expansions of the Wiener-Volterra autoregressive terms account for the long-term nonlinear information. As the proposed measures of entropy are instantaneously defined through probability functions, the novel indices are able to provide instantaneous tracking of the system complexity. The new measures are tested on synthetic data, as well as on real data gathered from heartbeat dynamics of healthy subjects and patients with cardiac heart failure and gait recordings from short walks of young and elderly subjects. Results show that instantaneous complexity is able to effectively track the system dynamics and is not affected by statistical noise properties.

Associations of physical activity, fitness, and body composition with heart rate variability-based indicators of stress and recovery on workdays: a cross-sectional study

BACKGROUND

The purpose of this study was to investigate how physical activity (PA), cardiorespiratory fitness (CRF), and body composition are associated with heart rate variability (HRV)-based indicators of stress and recovery on workdays. Additionally, we evaluated the association of objectively measured stress with self-reported burnout symptoms.

METHODS

Participants of this cross-sectional study were 81 healthy males (age range 26-40 y). Stress and recovery on workdays were measured objectively based on HRV recordings. CRF and anthropometry were assessed in laboratory conditions. The level of PA was based on a detailed PA interview (MET index [MET-h/d]) and self-reported activity class.

RESULTS

PA, CRF, and body composition were significantly associated with levels of stress and recovery on workdays. MET index (P < 0.001), activity class (P = 0.001), and CRF (P = 0.019) were negatively associated with stress during working hours whereas body fat percentage (P = 0.005) was positively associated. Overall, 27.5% of the variance of total stress on workdays (P = 0.001) was accounted for by PA, CRF, and body composition. Body fat percentage and body mass index were negatively associated with night-time recovery whereas CRF was positively associated. Objective work stress was associated (P = 0.003) with subjective burnout symptoms.

CONCLUSIONS

PA, CRF, and body composition are associated with HRV-based stress and recovery levels, which needs to be taken into account in the measurement, prevention, and treatment of work-related stress. The HRV-based method used to determine work-related stress and recovery was associated with self-reported burnout symptoms, but more research on the clinical importance of the methodology is needed.

Post-surgical analgesia in rainbow trout: is reduced cardioventilatory activity a sign of improved animal welfare or the adverse effects of an opioid drug?

The use of fish models in biomedical research is increasing. Since behavioural and physiological consequences of surgical procedures may affect experimental results, these effects should be defined and, if possible, ameliorated. Thus, the use of post-surgical analgesia should be considered after invasive procedures also in fish, but presently, little information exists on the effects of analgesics in fish. This study assessed the effects of an opioid drug, buprenorphine (0.05 mg/kg IM), on resting ventilation and heart rates during 7 days of postsurgical recovery in rainbow trout (Oncorhynchus mykiss) at 10°C by non-invasively recording bioelectric potentials from the fish via electrodes in the water. Baseline ventilation and heart rates were considerably lower compared to previously reported values for rainbow trout at 10°C, possibly due to the non-invasive recording technique. Buprenorphine significantly decreased both ventilation and heart rates further, and the effects were most pronounced at 4–7 days after anaesthesia, surgical procedures and administration of the drug. Somewhat surprisingly, the same effects of buprenorphine were seen in the two control groups that had not been subject to surgery. These results indicate that the reductions in ventilation and heart rates are not caused by an analgesic effect of the drug, but may instead reflect a general sedative effect acting on both behaviour as well as e.g. central control of ventilation in fishes. This resembles what has previously been demonstrated in mammals, although the duration of the drug effect is considerably longer in this ectothermic animal. Thus, before using buprenorphine for postoperative analgesic treatment in fish, these potentially adverse effects need further characterisation.

Developing a real time electrocardiogram system using virtual bio-instrumentation

Today bio-manufacturers propose various electrocardiogram (ECG) instruments that have addressed a wide variety of clinical issues. However, the discovery of new applications in ECG devices that provide doctors with the right information at the right time and in the right way will help them to provide a highest quality care possible. In this paper, we focus on the development of an accurate and robust virtual bio-instrument. The important goals of the described project is to provide online new diagnostic informations, an accurate analysis algorithm applied to the acquired signals, data capture from commercial monitors, fast real time ECG acquisition, real time data display and recording of real ECG signals which results in the improvement of data availability. The virtual bio-instrument is validated and tested on the level of robustness, diagnostic accuracy, diagnostic impact and Human - System Interface (HSI) functioning with collaboration of the cardiologists.

Cardiopulmonary coupling analysis: changes before and after treatment with a mandibular advancement device

PURPOSE

The aim of this study is to evaluate the changes of sleep quality in patients using a mandibular advancement device (MAD) for obstructive sleep apnea (OSA) based upon cardiopulmonary coupling (CPC).

METHODS

A total of 52 patients (mean age 53.7±9.6 years, range 33-74 years) were included in this study. Of them, there were 47 males (90.4%). All subjects were diagnosed with OSA after in-laboratory full-night polysomnography and reevaluated after 3-month use of a MAD. At baseline, apnea-hypopnea index (AHI) was 33.6±17.0, Epworth sleepiness scale was 10.5±4.8, and Pittsburgh sleep quality index was 5.8±2.8. The CPC parameters were extracted from single-lead electrocardiography of polysomnography. We compared CPC parameters at baseline with those after 3-month use of a MAD.

RESULTS

All respiratory indices improved with the use of MAD. However, there were no differences in the sleep architectures except N3 sleep (3.7±4.3 to 6.9±6.4%, p<0.001). The CPC parameters showed a significant improvement with the use of MAD. Low-frequency coupling (59.5±16.1 to 47.7±14.8%, p<0.001) and elevated low-frequency coupling (44.6±18.4 to 32.6±15.7%, p<0.001) significantly decreased. High-frequency coupling (28.6±16.0 to 36.5±15.7%, p=0.004) and very low frequency coupling (11.7±7.2 to 15.3±6.6%, p=0.028) significantly increased. The change of AHI significantly correlated with changes of the CPC parameters: negatively correlated with high-frequency coupling change (r=-0.572, p<0.001) and positively correlated with low-frequency and elevated low-frequency coupling changes (r=0.604 and 0.497, respectively; p<0.001 in both). However, the changes of Epworth sleepiness scale and Pittsburgh sleep quality index after MAD therapy showed no significant correlation with the changes in the CPC parameters.

CONCLUSIONS

To our knowledge, this is the first study to evaluate the quality of sleep in patients using a MAD for their OSA based upon CPC analysis. Low-frequency coupling decreased as AHI improved, while high-frequency coupling increased as AHI improved. The CPC parameters showed that the sleep quality was improved by MAD therapy.

A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms

Sleep scoring is one of the most important diagnostic methods in psychiatry and neurology. Sleep staging is a time consuming and difficult task undertaken by sleep experts. This study aims to identify a method which would classify sleep stages automatically and with a high degree of accuracy and, in this manner, will assist sleep experts. This study consists of three stages: feature extraction, feature selection from EEG signals, and classification of these signals. In the feature extraction stage, it is used 20 attribute algorithms in four categories. 41 feature parameters were obtained from these algorithms. Feature selection is important in the elimination of irrelevant and redundant features and in this manner prediction accuracy is improved and computational overhead in classification is reduced. Effective feature selection algorithms such as minimum redundancy maximum relevance (mRMR); fast correlation based feature selection (FCBF); ReliefF; t-test; and Fisher score algorithms are preferred at the feature selection stage in selecting a set of features which best represent EEG signals. The features obtained are used as input parameters for the classification algorithms. At the classification stage, five different classification algorithms (random forest (RF); feed-forward neural network (FFNN); decision tree (DT); support vector machine (SVM); and radial basis function neural network (RBF)) classify the problem. The results, obtained from different classification algorithms, are provided so that a comparison can be made between computation times and accuracy rates. Finally, it is obtained 97.03 % classification accuracy using the proposed method. The results show that the proposed method indicate the ability to design a new intelligent assistance sleep scoring system.

Detecting changes in retinal function: Analysis with Non-Stationary Weibull Error Regression and Spatial enhancement (ANSWERS)

Visual fields measured with standard automated perimetry are a benchmark test for determining retinal function in ocular pathologies such as glaucoma. Their monitoring over time is crucial in detecting change in disease course and, therefore, in prompting clinical intervention and defining endpoints in clinical trials of new therapies. However, conventional change detection methods do not take into account non-stationary measurement variability or spatial correlation present in these measures. An inferential statistical model, denoted ‘Analysis with Non-Stationary Weibull Error Regression and Spatial enhancement’ (ANSWERS), was proposed. In contrast to commonly used ordinary linear regression models, which assume normally distributed errors, ANSWERS incorporates non-stationary variability modelled as a mixture of Weibull distributions. Spatial correlation of measurements was also included into the model using a Bayesian framework. It was evaluated using a large dataset of visual field measurements acquired from electronic health records, and was compared with other widely used methods for detecting deterioration in retinal function. ANSWERS was able to detect deterioration significantly earlier than conventional methods, at matched false positive rates. Statistical sensitivity in detecting deterioration was also significantly better, especially in short time series. Furthermore, the spatial correlation utilised in ANSWERS was shown to improve the ability to detect deterioration, compared to equivalent models without spatial correlation, especially in short follow-up series. ANSWERS is a new efficient method for detecting changes in retinal function. It allows for better detection of change, more efficient endpoints and can potentially shorten the time in clinical trials for new therapies.