Short-term heart rate complexity is reduced in patients with type 1 diabetes mellitus

Vagal cardiac control in rats with LPS-induced lung injury

Heart rate variability (HRV) as an index of cardiac autonomic control in acute lung injury (ALI) has been evaluated in anaesthetized rats intratracheally instilled with bacterial lipopolysaccharide (LPS) and ventilated with breathing frequency of 60/min, 40% oxygen, inspiratory time 40%, tidal volume of 6 mL/kg. ECG was recorded before and 30, 60, 120, 180 and 240 min after LPS or saline (control) administration. HRV was quantified by time and frequency-domain analysis (mean RR interval, SDRR, RMSSD and spectral power in high frequency (HF) band. Lactate in plasma, and oxidative stress, IL-1β, IL-5, IL-12p70 and IL-13 and galectin-3 in heart tissue raised in LPS-injured rats. Overall HRV magnitude (SDRR) and marker of vagal heart rate control (RMSSD), as well as frequency domain parameter, spectral power HF was increased 120 and 180 min since ALI onset. In conclusion, LPS-induced ALI is accompanied by altered vagal cardiac control mediated by autonomic nervous system, likely based on the close relationship between immune response and vagally mediated autonomic nervous activity.

Symbolic dynamics of sleep heart rate variability is associated with cognitive decline in older men

We aimed to investigate the association between autonomic and cognitive functions in older men. We investigated heart rate variability (HRV) during sleep using time domain metrics and symbolic dynamics analysis of inter-beat intervals. These metrics were statistically analysed for associations with cognitive function which was elicited by administering the modified mini-mental state examination (3MS) and the Trail making test part-B in older men participating in the MrOS sleep study.Multivariable linear regression adjusted for age, body-mass-index (BMI), apnea-hypopnea index (AHI) and arousal index (A.I.) showed that symbolic dynamics of HRV especially the 0V% which is a measure of sympathetic outflow to the heart during rapid eye movement (REM) sleep is significantly associated with 3MS and Trail B scores. In conclusion, nonlinear HRV during sleep provides a unique window to probe the association between cognitive and autonomic function.Clinical Relevance- This study shows that cognitive decline is associated with altered cardiac autonomic function.

A fast sample entropy for pulse rate variability analysis

Sample entropy is an effective nonlinear index for analyzing pulse rate variability (PRV) signal, but it has problems with a large amount of calculation and time consumption. Therefore, this study proposes a fast sample entropy calculation method to analyze the PRV signal according to the microprocessor process of data updating and the principle of sample entropy. The simulated data and PRV signal are employed as experimental data to verify the accuracy and time consumption of the proposed method. The experimental results on simulated data display that the proposed improved sample entropy can improve the operation rate of the entropy value by a maximum of 47.6 times and an average of 28.6 times and keep the entropy value unchanged. Experimental results on PRV signal display that the proposed improved sample entropy has great potential in the real-time processing of physiological signals, which can increase approximately 35 times.

Heart Rate Variability for the Early Detection of Cardiac Autonomic Dysfunction in Type 1 Diabetes

Type 1 diabetes mellitus (T1DM) has an important impact on morbidity and mortality because it may start early in life. Therefore, the early detection of cardiovascular autonomic neuropathy (DCAN) in T1DM patients is important to intervene quickly and prevent further deterioration. Traditional autonomic function tests detect abnormalities in severely symptomatic patients but they are difficult to be standardized, require the patient’s active participation and their sensitivity to the early disease is limited. In comparison, heart rate variability (HRV) is easier to be measured and standardized. Therefore, we aim to find the HRV indexes that better identify DCAN at an early stage in T1DM patients, and evaluate if HRV is a valid alternative to traditional tests. For this aim, we administered the SCOPA-AUT questionnaire on symptoms of autonomic dysfunction as well as deep breathing, Valsalva, handgrip, head-up tilt (HUT), and cold-pressor tests, to 52 T1DM patients and 27 controls. We calculated HRV indexes during supine rest (SUP) and HUT, assessing differences between groups and postures by a linear mixed-effect model for repeated measures. Receiver Operating Characteristic (ROC) analysis quantified how each HRV index and autonomic test distinguishes between patients and controls. We found that the SCOPA-AUT score was slightly but significantly (p < 0.05) greater in patients, indicating an early DCAN. T1DM patients preserved the HRV response to changing posture but in SUP they showed significantly lower standard deviation and vagal indexes of HRV than controls. The area under the ROC curve of these HRV indexes was not lower than 0.68. By contrast, traditional autonomic tests did not differ between groups. Therefore, early DCAN initially causes an impairment of the cardiac vagal control manifest in conditions of elevated vagal tone, as in SUP. Compensatory adjustments of the sympathetic control might explain the unaltered response to traditional autonomic tests. In conclusion, vagal HRV indexes in SUP help to identify early DCAN better than traditional tests, potentially allowing rapid interventions.

Reduced System Complexity of Heart Rate Dynamics in Patients with Hyperthyroidism: A Multiscale Entropy Analysis

Studying heart rate dynamics would help understand the effects caused by a hyperkinetic heart in patients with hyperthyroidism. By using a multiscale entropy (MSE) analysis of heart rate dynamics derived from one-channel electrocardiogram recording, we aimed to compare the system complexity of heart rate dynamics between hyperthyroid patients and control subjects. A decreased MSE complexity index (CI) computed from MSE analysis reflects reduced system complexity. Compared with the control subjects (n = 37), the hyperthyroid patients (n = 37) revealed a significant decrease (p < 0.001) in MSE CI (hyperthyroid patients 10.21 ± 0.37 versus control subjects 14.08 ± 0.21), sample entropy for each scale factor (from 1 to 9), and high frequency power (HF) as well as a significant increase (p < 0.001) in low frequency power (LF) in normalized units (LF%) and ratio of LF to HF (LF/HF). In conclusion, besides cardiac autonomic dysfunction, the system complexity of heart rate dynamics is reduced in hyperthyroidism. This finding implies that the adaptability of the heart rate regulating system is impaired in hyperthyroid patients. Additionally, it might explain the exercise intolerance experienced by hyperthyroid patients. In addition, hyperthyroid patients and control subjects could be distinguished by the MSE CI computed from MSE analysis of heart rate dynamics.

Effects of ECG Data Length on Heart Rate Variability among Young Healthy Adults

The relationship between the robustness of HRV derived by linear and nonlinear methods to the required minimum data lengths has yet to be well understood. The normal electrocardiography (ECG) data of 14 healthy volunteers were applied to 34 HRV measures using various data lengths, and compared with the most prolonged (2000 R peaks or 750 s) by using the Mann-Whitney U test, to determine the 0.05 level of significance. We found that SDNN, RMSSD, pNN50, normalized LF, the ratio of LF and HF, and SD1 of the Poincaré plot could be adequately computed by small data size (60-100 R peaks). In addition, parameters of RQA did not show any significant differences among 60 and 750 s. However, longer data length (1000 R peaks) is recommended to calculate most other measures. The DFA and Lyapunov exponent might require an even longer data length to show robust results. Conclusions: Our work suggests the optimal minimum data sizes for different HRV measures which can potentially improve the efficiency and save the time and effort for both patients and medical care providers.

Influence of Different Types of Corticosteroids on Heart Rate Variability of Individuals with Duchenne Muscular Dystrophy-A Pilot Cross Sectional Study

Individuals with Duchenne Muscular Dystrophy (DMD) have an impairment of cardiac autonomic function categorized by parasympathetic reduction and sympathetic predominance. The objective of this study was to assess the cardiac autonomic modulation of individuals with DMD undergoing therapy with Prednisone/Prednisolone and Deflazacort and compare with individuals with DMD without the use of these medications and a typically developed control group. Methods: A cross-sectional study was completed, wherein 40 boys were evaluated. The four treatment groups were: Deflazacort; Prednisone/Prednisolone; no corticoid use; and typical development. Heart Rate Variability (HRV) was investigated via linear indices (Time Domain and Frequency Domain) and non-linear indices Results: The results of this study revealed that individuals with DMD undertaking pharmacotherapies with Prednisolone demonstrated HRV comparable to the Control Typically Developed (CTD) group. In contrast, individuals with DMD undergoing pharmacotherapies with Deflazacort achieved lower HRV, akin to individuals with DMD without any medications, as demonstrated in the metrics: RMSSD; LF (n.u.), HF (n.u.), LF/HF; SD1, α1, and α1/α2, and a significant effect for SD1/SD2; %DET and Ratio; Shannon Entropy, 0 V%, 2 LV% and 2 ULV%. Conclusions: Corticosteroids have the potential to affect the cardiac autonomic modulation in adolescents with DMD. The use of Prednisone/Prednisolone appears to promote improved responses in terms of sympathovagal activity as opposed to Deflazacort.

Ultra-short heart rate variability reliability for cardiac autonomic tone assessment in mesial temporal lobe epilepsy

Autonomic dysfunction in epilepsy is well-described. Heart rate variability (HRV) is a useful method to evaluate autonomic cardiac tone. Cardiac dysfunction may be involved in sudden unexpected death in epilepsy (SUDEP). HRV is a promising biomarker to enlighten the heart-brain axis role in SUDEP, but the required duration for a proper HRV recording in clinical routine remains unknown. This study aimed to verify the reliability of ultra-short HRV indices to evaluate cardiac autonomic tone in patients with epilepsy (PWE). Thirty-nine patients with mesial temporal lobe epilepsy (MTLE) had electrocardiogram recordings during the first day of video-EEG. Pearson's correlations were performed to evaluate the association between ultra-short HRV indices (five 1-min and five 30-s epochs) with standard time recording (5-min) and ANOVA compared the differences between mean HRV indices across epochs. Time domain (TD) indices showed higher mean r values when compared to frequency domain (FD) indices in 1-min (TD: r 0.80-0.99, FD: r 0.61-0.95) and 30-s epochs (TD: r 0.69-0.99, only high frequency: mean r values of 0.96). ANOVA evidenced that standard deviation of RR intervals and very low frequency means had at least 3 epochs significantly different for 1-min and 30-s epochs. Root mean square of the successive differences of RR intervals (rMSSD) presented higher Pearson's coefficient values and lower percentage of variation at 1-min or 30-s epochs in comparison to other HRV indices. In conclusion, rMSSD is the most reliable ultra-short HRV index for cardiac autonomic tone assessment in MTLE. The prognostic value of ultra-short HRV for cardiovascular risk evaluation in epilepsy remains to be determined in future studies.

Effect of aromatherapy on autonomic nervous system regulation with treadmill exercise-induced stress among adolescents

Introduction

Stress is a major health issue in adolescents owing to the important transitions experienced during this period. Aromatherapy is an effective method for the reduction of stress in adolescents.

Purpose

The aims of this study were to examine the effect of aromatherapy on the regulation of the autonomic nervous system (ANS) along with stress relief and to explore the effect of aromatherapy on adolescents with different levels of stress.

Methods

This quasi-experimental study comprised three types of treatments: control (no essential oil), pure essential oil therapy (sandalwood), and blended essential oil therapy (sandalwood-lavender). The heart rate variability (HRV) was calculated to evaluate the post-exercise recovery of the ANS to the baseline level in the recruited adolescents. To examine the efficiency of aromatherapy, Friedman test was used to assess the significance of difference in all parameters (i.e., mean heart rate, SDNN, normalized LF, normalized HF, and LF/HF) between baseline and after exercise among the three treatment conditions.

Results

The participants comprised 43 junior college students (8 males and 35 females) with a mean age of 18.21 ± 0.99. Significant differences in changes of two HRV parameters (normalized LF and LF/HF) were associated with both essential oil therapies compared to those in the control group (p<0.05), and one more HRV parameter (normalized HF) exhibited significant difference related to blended essential oil therapy compared to that of the control group. Besides, changes in two HRV parameters (mean heart rate and normalized HF) of both essential oil therapies in the low level stress subgroup showed significant differences compared to those of the control group (p<0.05).

Conclusions

This study demonstrated that aromatherapy could be used for ANS regulation with stress-relieving effects in adolescents. The participants with a low stress level appeared to respond better to the blended essential oil therapy, whereas those with medium to high levels of stress appeared to respond poorly to aromatherapy compared to the control.

Diffusion Entropy vs. Multiscale and Rényi Entropy to Detect Progression of Autonomic Neuropathy

We review the literature to argue the importance of the occurrence of crucial events in the dynamics of physiological processes. Crucial events are interpreted as short time intervals of turbulence, and the time distance between two consecutive crucial events is a waiting time distribution density with an inverse power law (IPL) index μ, with μ < 3 generating non-stationary behavior. The non-stationary condition is characterized by two regimes of the IPL index: (a) perennial non-stationarity, with 1 < μ < 2 and (b) slow evolution toward the stationary regime, with 2 < μ < 3. Human heartbeats and brain dynamics belong to the latter regime, with healthy physiological processes tending to be closer to the border with the perennial non-stationary regime with μ = 2. The complexity of cognitive tasks is associated with the mental effort required to address a difficult task, which leads to an increase of μ with increasing task difficulty. On this basis we explore the conjecture that disease evolution leads the IPL index μ moving from the healthy condition μ = 2 toward the border with Gaussian statistics with μ = 3, as the disease progresses. Examining heart rate time series of patients affected by diabetes-induced autonomic neuropathy of varying severity, we find that the progression of cardiac autonomic neuropathy (CAN) indeed shifts μ from the border with perennial variability, μ = 2, to the border with Gaussian statistics, μ = 3 and provides a novel, sensitive index for assessing disease progression. We find that at the Gaussian border, the dynamical complexity of crucial events is replaced by Gaussian fluctuation with long-time memory.

Cardiac autonomic modulation in type 1 diabetes during exercise-heat stress

AIMS

Cardiac autonomic modulation, as assessed by heart rate variability (HRV), is independently attenuated by both type 1 diabetes (T1D) and exercise-heat stress, although their combined effects remain unclear. We therefore assessed HRV during exercise-heat stress in young individuals (18-37 years) with (n = 14) and without type 1 diabetes (n = 14).

METHODS

Participants completed 30-min seated rest and three, 30-min bouts of semi-recumbent cycling at light, moderate, and vigorous metabolic heat productions (200, 250, 300 W/m², respectively), each followed by 30-min recovery. Body core temperature (T_core) and electrocardiogram were recorded throughout and analyzed during the final 5-min of rest and each exercise period.

RESULTS

Relative to baseline, T_core was increased in both groups, albeit to a greater extent in type 1 diabetes during vigorous exercise (T1D, 1.1 ± 0.3 °C; control, 0.8 ± 0.3 °C; P < 0.05). Overall HRV (as reflected by entropy) was attenuated throughout exercise relative to baseline in both groups, with the magnitude of the reduction greater in type 1 diabetes during vigorous exercise (T1D, - 108%; control, - 70%; P < 0.05).

CONCLUSIONS

Given the negative correlations between decreased HRV and cardiac risk, our novel observations indicate that vigorous exercise in hot environments may pose a health concern for individuals with type 1 diabetes.

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.

MULTISCALE ENTROPY AND MULTISCALE TIME IRREVERSIBILITY ANALYSIS OF RR TIME SERIES DEPENDING ON AMBIENT TEMPERATURE

Purpose: The main aim of this paper is to study the influence of temperature on multiscale entropy (MSE) and multiscale time irreversibility (MTI) through the use of short-term measurements. Methods: A total of 12 physically active, healthy, and nonsmoker individuals ([Formula: see text] years old; [Formula: see text][Formula: see text]cm of height; and [Formula: see text][Formula: see text]kg of body mass) voluntarily participated in this study. Two beat-to-beat recordings of 15[Formula: see text]min length were performed on every participant, one under hot conditions ([Formula: see text]C) and the other assessment under cool conditions ([Formula: see text]C). The order of these two assessments was randomly assigned. Multiscale sample entropy and MTI were assessed in every measurement through 10 scales. Results: Entropy was significantly higher under hot conditions ([Formula: see text]) from the fifth scale compared to cool conditions. On the contrary, MTI values were significantly lower under hotter conditions ([Formula: see text]). Conclusions: The study of MSE and time irreversibility of short RR measurements presents consistent and reliable data. Moreover, exposures to hot conditions provoke an increment of interbeat complexity throughout larger scales and a decrease in the MTI in a healthy population.

Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

Effects of Metformin Added to Insulin in Adolescents with Type 1 Diabetes: An Exploratory Crossover Randomized Trial

BACKGROUND

To comprehensively assess the effects of metformin added to insulin on metabolic control, insulin sensitivity, and cardiovascular autonomic function in adolescents with type 1 diabetes.

MATERIALS AND METHODS

This was an exploratory, crossover, randomized trial conducted in adolescents with type 1 diabetes aged 12-18 years old. Participants were randomly received metformin (≤1000 mg/d) added to insulin for 24 weeks followed by insulin monotherapy for a subsequent 24 weeks or vice versa. Blood pressure, body mass index, insulin dose, estimated insulin sensitivity, glycated hemoglobin A1c (HbA1c), and lipid profiles were measured, with a 72-hour continuous glucose monitoring and 24-hour Holter monitoring performed at baseline, 24, and 50 weeks for the assessments of glucose variability and heart rate variability.

RESULTS

Seventeen patients with mean ± SD age 14.4 ± 2.3 years, body mass index 18.17 ± 1.81 kg/m², median (IQR) diabetes duration 4.50 (3.58, 6.92) years, and HbA1c 9.0% (8.5%, 9.4%) were enrolled. The between-group difference in HbA1c of 0.28% (95% CI -0.39 to 0.95%) was not significant (P = 0.40). Changes in body mass index, insulin dose, blood pressure, lipid profiles, and estimated insulin sensitivity were similar for metformin add-on vs. insulin monotherapy. Glucose variability also did not differ. Compared with insulin monotherapy, metformin add-on significantly increased multiple heart rate variability parameters.

CONCLUSIONS

Metformin added to insulin did not improve metabolic control or glucose variability in lean/normal-weight adolescents with type 1 diabetes. However, metformin added to insulin significantly increased heart rate variability, suggesting that metformin might improve cardiovascular autonomic function in this population.

Resting Cardiac Vagal Tone is Associated with Long-Term Frustration Level of Mental Workload: Ultra-short Term Recording Reliability

Excessive mental workload represent a critical risk factor for workplace accidents. Heart rate variability (HRV) is a non-invasive low cost electrophysiological autonomic biomarker related to emotional and cognitive regulation. Several studies report that mental overload impairs parasympathetic-mediated HRV indices (e.g. rMSSD). However, the influence of resting state HRV as a predictor of long-term mental workload impairments remains unknown. Thirty participants (22 males; 8 females) had their HRV measured (5-min period) before performing the number search task. After the task, the mental load was accessed by the NASA-TLX questionnaire. A simple linear regression model between HRV and NASA-TLX dimensions showed that resting state rMSSD is associated to physical demand (ND-2, R² = 0.143, p = 0.03) and frustration level (ND-6, R² = 0.175, p = 0.02) dimensions of mental workload. The comparison between 1 and 5-min epochs suggests that regression models remain reliable even using the ultra-short term HRV (< 1 min) recording values (R² values from 0.11 to 0.15 for ND-2 and R² values from 0.16 to 0.19 for ND-6). These results suggest that resting state HRV is associated to long-term effects of mental workload on physical and emotional demands. In addition, the ultra-short term HRV indices remains reliable to assess ND-2 and ND-6 dimensions of mental workload when compared to gold-standard time interval (> 5 min). The resting state cardiac autonomic tone assessment optimizes the physiological approach with a quick, non-invasive and low-cost assessment that can provide insights about mental load adjustments to prevent work-related accidents.

Heart rate variability changes in major depressive disorder during sleep: Fractal index correlates with BDI score during REM sleep

We investigated the relationship between autonomic nervous system activity during each sleep stage and the severity of depressive symptoms in patients with major depressive disorder (MDD) and healthy control subjects. Thirty patients with MDD and thirty healthy control subjects matched for sex, age, and body mass index completed standard overnight polysomnography. Depression severity was assessed using the Beck Depression Inventory (BDI). Time- and frequency-domain, and fractal HRV parameters were derived from 5-min electrocardiogram segments during light sleep, deep sleep, rapid eye movement (REM) sleep, and the pre- and post-sleep wake periods. Detrended fluctuation analysis (DFA) alpha-1 values during REM sleep were significantly higher in patients with MDD than in control subjects, and a significant correlation existed between DFA alpha-1 and BDI score in all subjects. DFA alpha-1 was the strongest predictor for the BDI score, along with REM density as a covariate. This study found that compared with controls, patients with MDD show reduced complexity in heart rate during REM sleep, which may represent lower cardiovascular adaptability in these patients, and could lead to cardiac disease. Moreover, DFA alpha-1 values measured during REM sleep may be useful as an indicator for the diagnosis and monitoring of depression.

A unifying conceptual framework of factors associated to cardiac vagal control

Cardiac vagal control (CVC) reflects the activity of the vagus nerve regulating cardiac functioning. CVC can be inferred via heart rate variability measurement, and it has been positively associated to a broad range of cognitive, emotional, social, and health outcomes. It could then be considered as an indicator for effective self-regulation, and given this role, one should understand the factors increasing and decreasing CVC. The aim of this paper is to review the broad range of factors influencing CVC, and to provide a unifying conceptual framework to integrate comprehensively those factors. The structure of the unifying conceptual framework is based on the theory of ecological rationality, while its functional aspects are based on the neurovisceral integration model. The structure of this framework distinguishes two broad areas of associations: person and environment, as this reflects adequately the role played by CVC regarding adaptation. The added value of this framework lies at different levels: theoretically, it allows integrating findings from a variety of scientific disciplines and refining the predictions of the neurovisceral integration model; methodologically, it helps identifying factors that increase and decrease CVC; and lastly at the applied level, it can play an important role for society regarding health policies and for the individual to empower one's flourishing.

A unified non-linear approach based on recurrence quantification analysis and approximate entropy: application to the classification of heart rate variability of age-stratified subjects

This paper presents a unified approach based on the recurrence quantification analysis (RQA) and approximate entropy (ApEn) for the classification of heart rate variability (HRV). In this paper, the optimum tolerance threshold (r_opt) corresponding to ApEn_max has been used for RQA calculation. The experimental data length (N) of RR interval series (RR_i) is optimized by taking r_opt as key parameter. r_opt is found to be lying within the recommended range of 0.1 to 0.25 times the standard deviation of the RR_i, when N ≥ 300. Consequently, RQA is applied to the age stratified RR_i and indices such as percentage recurrence (%REC), percentage laminarity (%LAM), and percentage determinism (%DET) are calculated along with ApEn_max, [Formula: see text], [Formula: see text], and an index namely the radius differential (R_D). Certain standard HRV statistical indices such as mean RR, standard deviation of RR (or NN) interval (SDNN), and the square root of the mean squared differences of successive RR intervals (RMSSD) (Eur Hear J 17:354-381, 1996) are also found for comparison. It is observed that (i) R_D can discriminate between the elderly and young subjects with a value of 0.1151 ± 0.0236 (mean ± SD) and 0.0533 ± 0.0133 (mean ± SD) respectively for the elderly and young subjects and is found to be statistically significant with p < 0.05. (ii) Similar significant discrimination was obtained using [Formula: see text] with a value of 0.1827 ± 0.0382 (mean ± SD) and 0.2248 ± 0.0320 (mean ± SD) (iii) other significant indices were found to be %REC, %DET, %LAM, SDNN, and RMSSD; however, ApEn_max was found to be insignificant with p > 0.05. The above features of RR_i time series were tested for classification using support vector machine (SVM) and multilayer perceptron neural network (MLPNN). Higher classification accuracy was achieved using SVM with a maximum value of 99.71%. Graphical abstract.

A Heartbeat Away From Consciousness: Heart Rate Variability Entropy Can Discriminate Disorders of Consciousness and Is Correlated With Resting-State fMRI Brain Connectivity of the Central Autonomic Network

Background: Disorders of consciousness are challenging to diagnose, with inconsistent behavioral responses, motor and cognitive disabilities, leading to approximately 40% misdiagnoses. Heart rate variability (HRV) reflects the complexity of the heart-brain two-way dynamic interactions. HRV entropy analysis quantifies the unpredictability and complexity of the heart rate beats intervals. We here investigate the complexity index (CI), a score of HRV complexity by aggregating the non-linear multi-scale entropies over a range of time scales, and its discriminative power in chronic patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), and its relation to brain functional connectivity. Methods: We investigated the CI in short (CIs) and long (CIl) time scales in 14 UWS and 16 MCS sedated. CI for MCS and UWS groups were compared using a Mann-Whitney exact test. Spearman's correlation tests were conducted between the Coma Recovery Scale-revised (CRS-R) and both CI. Discriminative power of both CI was assessed with One-R machine learning model. Correlation between CI and brain connectivity (detected with functional magnetic resonance imagery using seed-based and hypothesis-free intrinsic connectivity) was investigated using a linear regression in a subgroup of 10 UWS and 11 MCS patients with sufficient image quality. Results: Higher CIs and CIl values were observed in MCS compared to UWS. Positive correlations were found between CRS-R and both CI. The One-R classifier selected CIl as the best discriminator between UWS and MCS with 90% accuracy, 7% false positive and 13% false negative rates after a 10-fold cross-validation test. Positive correlations were observed between both CI and the recovery of functional connectivity of brain areas belonging to the central autonomic networks (CAN). Conclusion: CI of MCS compared to UWS patients has high discriminative power and low false negative rate at one third of the estimated human assessors' misdiagnosis, providing an easy, inexpensive and non-invasive diagnostic tool. CI reflects functional connectivity changes in the CAN, suggesting that CI can provide an indirect way to screen and monitor connectivity changes in this neural system. Future studies should assess the extent of CI's predictive power in a larger cohort of patients and prognostic power in acute patients.

Influence of Type 1 Diabetes on the Symbolic Analysis and Complexity of Heart Rate Variability in Young Adults

Background

Type 1 diabetes mellitus can cause autonomic changes, which can be assessed by heart rate variability. Among the heart rate variability assessment methods, the symbolic analysis and Shannon entropy, based on the Chaotic dynamics, have gained prominence.

Objective

To compare heart rate variability indexes, obtained through symbolic analysis and Shannon entropy, in young adults with type 1 diabetes mellitus and healthy young individuals, associated with the analysis of linear indexes; and to verify if there are associations between the indexes obtained by the symbolic analysis and by Shannon entropy and linear indexes in diabetic individuals.

Methods

Heart rate variability data from 39 young adults with type 1 diabetes mellitus and 43 healthy young individuals were analyzed, using a cardio-frequency meter. Linear indexes (standard deviation of all normal RR intervals recorded in a time interval expressed in milliseconds; square root of the mean of the squared differences between adjacent normal RR intervals in a time interval expressed in milliseconds; low and high frequency components in millisecond squared; and normalized units and ratio between low and high frequency components) and nonlinear ones (Shannon entropy and symbolic analysis - standard without variation; with one or two variations; and with two different variations) of the heart rate variability were calculated. The statistical significance was set at 5%, and the confidence interval was 95%.

Results

Significantly lower values were observed in the DM1 group compared to healthy young adults for the standard deviation indexes of all normal RR intervals recorded in a time interval [37.30 (29.90) vs. 64.50 (36.20); p = 0.0001], square root of the mean of the squared differences between adjacent normal RR intervals in a time interval [32.73 (17.43) vs. 55.59 (21.60); p = 0.0001], low frequency component [402.00 (531.00) vs. 1,203.00 (1,148.00); p = 0.0001], high frequency component [386.00 (583.00) vs. 963.00 (866.00); p = 0.0001] and the pattern with two different variations [15,33 (9,22) vs. 20.24 (12.73); p = 0.0114], with the effect of this difference being considered large (standard deviation of all normal RR intervals recorded in a time interval, square root of the mean of the squared differences between adjacent normal RR intervals in a time interval and low frequency component), medium (high frequency component) and small (standard with two different variations). The agreement of the associations between the linear and non-linear indexes was considered elevated for the high frequency component index - normalized units (r = -0.776), with the standard index without variation, and moderate for the indexes square root of the mean of the squared differences between adjacent normal RR intervals in a time interval (r = 0.550), standard deviation of all normal RR intervals recorded in a time interval (r = 0.522), high frequency component - normalized units (r = 0.638) with the index standard with two similar variations, as well as for the indexes square root of the mean of the squared differences between adjacent normal RR intervals in a time interval (r = 0.627) and high frequency component - normalized units (r = 0.601) with the index standard with two different variations.

Conclusion

Type 1 diabetes mellitus influenced linear indexes and symbolic analysis, but not yet in the complexity of heart rate variability. Additionally, heart rate variability indexes correlated with the symbolic dynamics.

Heart rate differences between symptomatic and asymptomatic Brugada syndrome patients at night

OBJECTIVE

Ventricular arrhythmias in Brugada syndrome (BS) mainly occur at rest, especially during nighttime, suggesting that parasympathetic activity at night may play an important role in the arrhythmogenesis of the disease. This study examined and compared the autonomic function of symptomatic and asymptomatic BS patients overnight.

APPROACH

We analyzed various heart rate variability (HRV) and heart rate complexity (HRC) markers in a clinical series including 87 BS patients, where 23 were symptomatic.

MAIN RESULTS

Statistically significant differences were found in markers MIRR, SDNN, SDANN, [Formula: see text] and SampEn, suggesting that symptomatic patients may be related to lower heart rate variability and complexity values, as well as to greater circadian fluctuations overnight.

SIGNIFICANCE

The results provide further evidence for the role of autonomic imbalance in the pathophysiology of BS, highlighting the relevance of nighttime analysis to the unmasking of significant ANS changes. Based on these outcomes, the role of HRV and HRC assessment at night could be a step forward towards the understanding of BS and the risk for the occurrence of symptoms in these patients, with a potential future impact on therapeutic strategies.

Respiratory sinus arrhythmia as a non-invasive index of 'brain-heart' interaction in stress

Respiratory sinus arrhythmia (RSA) is accepted as a peripheral marker of cardiac-linked parasympathetic regulation. According to polyvagal theory, the RSA is also considered as the index of emotion regulation. The neurovisceral integration model posits that parasympathetic modulation of the heart marked by RSA is related to complex nervous regulation associated with emotional and cognitive processing. From this perspective, high resting RSA amplitude associated with a greater withdrawal during stressors and subsequent recovery could represent a flexible and adaptive physiological response system to a challenge. Conversely, low resting RSA accompanied by an inadequate reactivity to stress might reflect maladaptive regulatory mechanisms. The RSA reactivity is different with various types of stressors: while the RSA decreases to cognitive tasks indicating a vagal withdrawal, the RSA magnitude increases to emotional challenge indicating an effective cognitive processing of emotional stimuli. The RSA reactivity to stress could have important implications for several mental disorders, e.g. depressive or anxiety disorder. It seems that the study of the RSA, as a non-invasive index of ‘brain-heart’ communication, could provide important information on the pathway linked to mental and physical health.

The neurovascular complexity index as a potential indicator of traumatic brain injury severity: A case-series study

BACKGROUND

Multimodal monitoring of brain physiology following a traumatic brain injury (TBI) shows promise as a strategy to improve management and outcomes of TBI patients within civilian and military trauma. Valid and reliable measures of different aspects of brain physiology following a TBI could prove critical to accurately capturing these changes.

METHODS

Using a case-series design with a control subject group comparison, we evaluated a new proprietary algorithm called the Neurovascular Complexity Index (NCI) using transcranial Doppler to noninvasively obtain measures of cerebral blood flow variability. Baseline NCI data from 169 control subjects were compared with 12 patients with moderate to severe TBI.

RESULTS

Patients with TBI exhibited significantly greater mean and variability in NCI scores compared with control subjects (F = 195.48; p < 0.001). The mean absolute deviation (MAD) of NCI scores increased significantly and in a monotonic fashion with severity of injury, where control subjects exhibited a small MAD of 0.44, patients with moderate TBI had a higher MAD of 4.20, and patients with severe TBI had an MAD of 6.51 (p < 0.001).

CONCLUSIONS

Advancement in multimodal monitoring of TBI patients is important in reducing the potential risk of secondary injury. This study reports results indicating that a new noninvasive quantifiable assessment of TBI based on a noninvasive measure of cerebral blood flow variability shows potential for continuous monitoring and early identification of brain-injured patients, deployable in far-forward military environments, to better inform individualized management.

LEVEL OF EVIDENCE

Case series, level IV.

Baroreflex Coupling Assessed by Cross-Compression Entropy

Estimating interactions between physiological systems is an important challenge in modern biomedical research. Here, we explore a new concept for quantifying information common in two time series by cross-compressibility. Cross-compression entropy (CCE) exploits the ZIP data compression algorithm extended to bivariate data analysis. First, time series are transformed into symbol vectors. Symbols of the target time series are coded by the symbols of the source series. Uncoupled and linearly coupled surrogates were derived from cardiovascular recordings of 36 healthy controls obtained during rest to demonstrate suitability of this method for assessing physiological coupling. CCE at rest was compared to that of isometric handgrip exercise. Finally, spontaneous baroreflex interaction assessed by CCE_BRS was compared between 21 patients suffering from acute schizophrenia and 21 matched controls. The CCE_BRS of original time series was significantly higher than in uncoupled surrogates in 89% of the subjects and higher than in linearly coupled surrogates in 47% of the subjects. Handgrip exercise led to sympathetic activation and vagal inhibition accompanied by reduced baroreflex sensitivity. CCE_BRS decreased from 0.553 ± 0.030 at rest to 0.514 ± 0.035 during exercise (p < 0.001). In acute schizophrenia, heart rate, and blood pressure were elevated. Heart rate variability indicated a change of sympathovagal balance. The CCE_BRS of patients with schizophrenia was reduced compared to healthy controls (0.546 ± 0.042 vs. 0.507 ± 0.046, p < 0.01) and revealed a decrease of blood pressure influence on heart rate in patients with schizophrenia. Our results indicate that CCE is suitable for the investigation of linear and non-linear coupling in cardiovascular time series. CCE can quantify causal interactions in short, noisy and non-stationary physiological time series.

Sensitivity, Specificity and Predictive Value of Heart Rate Variability Indices in Type 1 Diabetes Mellitus

Background

Heart rate variability (HRV) indices may detect autonomic changes with good diagnostic accuracy. Type diabetes mellitus (DM) individuals may have changes in autonomic modulation; however, studies of this nature in this population are still scarce.

Objective

To compare HRV indices between and assess their prognostic value by measurements of sensitivity, specificity and predictive values in young individuals with type 1 DM and healthy volunteers.

Methods

In this cross-sectional study, physical and clinical assessment was performed in 39 young patients with type 1 DM and 43 young healthy controls. For HRV analysis, beat-to-beat heart rate variability was measured in dorsal decubitus, using a Polar S810i heart rate monitor, for 30 minutes. The following indices were calculated: SDNN, RMSSD, PNN50, TINN, RRTri, LF ms², HF ms², LF un, HF un, LF/HF, SD1, SD2, SD1/SD2, and ApEn.

Results

Type 1 DM subjects showed a decrease in sympathetic and parasympathetic activities, and overall variability of autonomic nervous system. The RMSSD, SDNN, PNN50, LF ms², HF ms², RRTri, SD1 and SD2 indices showed greater diagnostic accuracy in discriminating diabetic from healthy individuals.

Conclusion

Type 1 DM individuals have changes in autonomic modulation. The SDNN, RMSSD, PNN50, RRtri, LF ms², HF ms², SD1 and SD2 indices may be alternative tools to discriminate individuals with type 1 DM.

Application of Heart Rate Variability in Diagnosis and Prognosis of Individuals with Diabetes Mellitus: Systematic Review

BACKGROUND

The use of heart rate variability as a tool capable of discriminating individuals with diabetes mellitus is still little explored, as its use has been limited to comparing those with and without the disease. Thus, the purpose of this study was to verify the use of heart rate variability as a tool for diagnostic and prognostic evaluation in person with diabetes and to identify whether there are cutoff points generated from the use of this tool in these individuals.

METHODS

A search was conducted in the electronic databases MEDLINE, Cochrane Library, Web of Science, EMBASE, and LILACS starting from the oldest records until January 2015, by means of descriptors related to the target condition, evaluated tool, and evaluation method. All the studies were evaluated for methodological quality using the QUADAS-2 instrument.

RESULTS

Eight studies were selected. In general, the studies showed that the heart rate variability is useful to discriminate cardiac autonomic neuropathy in person with diabetes, and the sample entropy, SD1/SD2 indices, SDANN, HF, and slope of TFC have better discriminatory power to detect autonomic dysfunction, with sensitivity and specificity values ranging from 72% to 100% and 71% to 97%, respectively.

CONCLUSION

Although there are methodological differences in indices used, in general, this tool demonstrated good sensitivity and specificity and can be used as an additional and/or complementary tool to the conventional autonomic tests, in order to obtain safer and more effective diagnostic, collaborating for better risk stratification conditions of these patients.

Dynamics of heart rate variability analysed through nonlinear and linear dynamics is already impaired in young type 1 diabetic subjects

BACKGROUND

Autonomic diabetic neuropathy is one of the most common complications of type 1 diabetes mellitus, and studies using heart rate variability to investigate these individuals have shown inconclusive results regarding autonomic nervous system activation. Aims To investigate the dynamics of heart rate in young subjects with type 1 diabetes mellitus through nonlinear and linear methods of heart rate variability.

METHODS

We evaluated 20 subjects with type 1 diabetes mellitus and 23 healthy control subjects. We obtained the following nonlinear indices from the recurrence plot: recurrence rate (REC), determinism (DET), and Shanon entropy (ES), and we analysed indices in the frequency (LF and HF in ms2 and normalised units - nu - and LF/HF ratio) and time domains (SDNN and RMSSD), through analysis of 1000 R-R intervals, captured by a heart rate monitor.

RESULTS

There were reduced values (p<0.05) for individuals with type 1 diabetes mellitus compared with healthy subjects in the following indices: DET, REC, ES, RMSSD, SDNN, LF (ms2), and HF (ms2). In relation to the recurrence plot, subjects with type 1 diabetes mellitus demonstrated lower recurrence and greater variation in their plot, inter-group and intra-group, respectively.

CONCLUSION

Young subjects with type 1 diabetes mellitus have autonomic nervous system behaviour that tends to randomness compared with healthy young subjects. Moreover, this behaviour is related to reduced sympathetic and parasympathetic activity of the autonomic nervous system.

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.

The application of information theory for the research of aging and aging-related diseases

This article reviews the application of information-theoretical analysis, employing measures of entropy and mutual information, for the study of aging and aging-related diseases. The research of aging and aging-related diseases is particularly suitable for the application of information theory methods, as aging processes and related diseases are multi-parametric, with continuous parameters coexisting alongside discrete parameters, and with the relations between the parameters being as a rule non-linear. Information theory provides unique analytical capabilities for the solution of such problems, with unique advantages over common linear biostatistics. Among the age-related diseases, information theory has been used in the study of neurodegenerative diseases (particularly using EEG time series for diagnosis and prediction), cancer (particularly for establishing individual and combined cancer biomarkers), diabetes (mainly utilizing mutual information to characterize the diseased and aging states), and heart disease (mainly for the analysis of heart rate variability). Few works have employed information theory for the analysis of general aging processes and frailty, as underlying determinants and possible early preclinical diagnostic measures for aging-related diseases. Generally, the use of information-theoretical analysis permits not only establishing the (non-linear) correlations between diagnostic or therapeutic parameters of interest, but may also provide a theoretical insight into the nature of aging and related diseases by establishing the measures of variability, adaptation, regulation or homeostasis, within a system of interest. It may be hoped that the increased use of such measures in research may considerably increase diagnostic and therapeutic capabilities and the fundamental theoretical mathematical understanding of aging and disease.

AN IMPROVED ONLINE PARADIGM FOR SCREENING OF DIABETIC PATIENTS USING RR-INTERVAL SIGNALS

Diabetes Mellitus (DM) which is a chronic disease and difficult to cure. If diabetes is not treated in a timely manner, it may cause serious complications. For timely treatment, an early detection of the disease is of great interest. Diabetes can be detected by analyzing the RR-interval signals. This work presents a methodology for classification of diabetic and normal RR-interval signals. Firstly, empirical mode decomposition (EMD) method is applied to decompose the RR-interval signals in to intrinsic mode functions (IMFs). Then five parameters namely, area of analytic signal representation (AASR), mean frequency computed using Fourier-Bessel series expansion (MFFB), area of ellipse evaluated from second-order difference plot (ASODP), bandwidth due to frequency modulation (BFM) and bandwidth due to amplitude modulation (BAM) are extracted from IMFs obtained from RR-interval signals. Statistically significant features are fed to least square-support vector machine (LS-SVM) classifier. The three kernels namely, Radial Basis Function (RBF), Morlet wavelet, and Mexican hat wavelet kernels have been studied to obtain the suitable kernel function for the classification of diabetic and normal RR-interval signals. In this work, we have obtained the highest classification accuracy of 95.63%, using Morlet wavelet kernel function with 10-fold cross-validation. The classification system proposed in this work can help the clinicians to diagnose diabetes using electrocardiogram (ECG) signals.

An adaptive technique for multiscale approximate entropy (MAEbin) threshold (r) selection: application to heart rate variability (HRV) and systolic blood pressure variability (SBPV) under postural stress

Multiscale approximate entropy (MAE) is used to quantify the complexity of a time series as a function of time scale τ. Approximate entropy (ApEn) tolerance threshold selection 'r' is based on either: (1) arbitrary selection in the recommended range (0.1-0.25) times standard deviation of time series (2) or finding maximum ApEn (ApEnmax) i.e., the point where self-matches start to prevail over other matches and choosing the corresponding 'r' (rmax) as threshold (3) or computing rchon by empirically finding the relation between rmax, SD1/SD2 ratio and N using curve fitting, where, SD1 and SD2 are short-term and long-term variability of a time series respectively. None of these methods is gold standard for selection of 'r'. In our previous study [1], an adaptive procedure for selection of 'r' is proposed for approximate entropy (ApEn). In this paper, this is extended to multiple time scales using MAEbin and multiscale cross-MAEbin (XMAEbin). We applied this to simulations i.e. 50 realizations (n = 50) of random number series, fractional Brownian motion (fBm) and MIX (P) [1] series of data length of N = 300 and short term recordings of HRV and SBPV performed under postural stress from supine to standing. MAEbin and XMAEbin analysis was performed on laboratory recorded data of 50 healthy young subjects experiencing postural stress from supine to upright. The study showed that (i) ApEnbin of HRV is more than SBPV in supine position but is lower than SBPV in upright position (ii) ApEnbin of HRV decreases from supine i.e. 1.7324 ± 0.112 (mean ± SD) to upright 1.4916 ± 0.108 due to vagal inhibition (iii) ApEnbin of SBPV increases from supine i.e. 1.5535 ± 0.098 to upright i.e. 1.6241 ± 0.101 due sympathetic activation (iv) individual and cross complexities of RRi and systolic blood pressure (SBP) series depend on time scale under consideration (v) XMAEbin calculated using ApEnmax is correlated with cross-MAE calculated using ApEn (0.1-0.26) in steps of 0.02 at each time scale in supine and upright position and is concluded that ApEn0.26 has highest correlation at most scales (vi) choice of 'r' is critical in interpreting interactions between RRi and SBP and in ascertaining true complexity of the individual RRi and SBP series.

A percentile-based coarse graining approach is helpful in symbolizing heart rate variability during graded head-up tilt

Coarse graining of physiological time series such as the cardiac interbeat interval series by means of a symbolic transformation retains information about dynamical properties of the underlying system and complements standard measures of heart rate variability. The transformations of the original time series to the coarse grained symbolic series usually lead to a non-uniform occurrence of the different symbols, i.e. some symbols appear more often than others influencing the results of the subsequent symbolic series analysis. Here, we defined a transformation procedure to assure that each symbol appears with equal probability using a short alphabet {0,1,2,3} and a long alphabet {0,1,2,3,4,5}. The procedure was applied to the cardiac interbeat interval series RRi of 17 healthy subjects obtained during graded head-up tilt testing. The symbolic dynamics is analyzed by means of the occurrence of short sequences (`words') of length 3. The occurrence of words is grouped according to words without variations of the symbols (0V%), one variation (1V%), two like variations (2LV%) and two unlike variations (2UV%). Linear regression analysis with respect to tilt angle showed that for the short alphabet 0V% increased with increasing tilt angle whereas 1V%, 2LV% and 2UV% decreased. For the long alphabet 0V%, and 1V% increased with increasing tilt angle whereas 2LV% and 2UV% decreased. These results were slightly better compared to the results from non-uniform symbolic transformations reflecting the deviation from the mean. In conclusion, the symbolic transformation assuring the appearance of symbols with equal probability is capable of reflecting changes of the cardiac autonomic nervous system during graded head-up tilt. Furthermore, the transformation is independent of the time series' distribution.

Multiscale entropic assessment of autonomic dysfunction in patients with obstructive sleep apnea and therapeutic impact of continuous positive airway pressure treatment

OBJECTIVE

Obstructive sleep apnea (OSA) is an independent risk factor for cardiovascular disease because of its associated autonomic nervous and vascular regulatory dysfunctions. We tested the hypothesis that the multiscale entropy (MSE) approach to heart rate variability analysis may be used for evaluating OSA severity through simultaneous assessment of these abnormalities.

METHODS

A total of 147 subjects were divided into four groups according to apnea-hypopnea index (AHI) from polysomnography (PSG): Snoring without OSA (5 > AHI, n = 31), mild (5 ≤ AHI < 15, n = 31), moderate (15 ≤ AHI < 30, n = 41), and severe (AHI ≥ 30, n = 44) OSA. Of the patients, 41 receiving continuous positive airway pressure (CPAP) treatment were included for comparison. For each subject, two segments of electrocardiographic (ECG) signals (both at stage N2) were used for R-R interval (RRI) analysis, including a 10-minute recording 10 minutes after falling asleep (ie, early phase) and another 10-minute segment at 3 hours (ie, late phase). Heart rate variability as reflected in changes in RRI between the two segments was assessed with small-scale multiscale entropy index (MEISS, sum of sample entropy from time scale from 1 to 5) and large-scale multiscale entropy index (MEILS, scale from 6 to 10).

RESULTS

Increase in MEILS in the late phase of sleep was noted in both the normal snoring and CPAP groups (P <0.01). Although the moderate OSA group exhibited MEISS drop in the late phase (P < 0.02), both MEISS and MEILS decreased in the late phase in the severe OSA group (P < 0.001, P < 0.02). However, no differences were noted in mild OSA subjects in both parameters.

CONCLUSION

The results demonstrated significant severity-dependent deterioration in autonomic and vascular regulatory function in patients with OSA as reflected in the reductions in MEISS and MEILS, respectively, and notable improvement after CPAP treatment. The MEI obtainable through PSG may indicate not only OSA severity and physiological status but also therapeutic outcome for OSA patients.

Heart Rate Variability and Inflammatory Response in Rats With Lipopolysaccharide-Induced Endotoxemia

The aim of the study was to evaluate short-term heart rate variability (HRV) as an index of cardiac autonomic control in rats with lipopolysaccharide (LPS)-induced endotoxemia. Animals were injected intraperitoneally with LPS (100 µg/kg b.w.) and control group with an equivalent volume of saline. ECG recordings were done before (base) and 60, 120, 180, 240 and 300 min after LPS or saline administration. HRV magnitude was quantified by time and frequency-domain analysis (mean RR interval, SDRR, RMSSD, spectral powers in low (LF) and high frequency (HF) bands. Heart tissue homogenates and plasma were analyzed to determine interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α) and oxidative stress level (TBARS). Administration of lipopolysaccharide was followed by continuous rise in colonic body temperature compared to saline-treated controls. Endotoxemia in rats was accompanied by significant decrease in HRV spectral activity in high-frequency range at maximal body temperature (logHFpower: 1.2±0.5 vs. 1.9±0.6 ms2, P<0.01). Increased IL-6 was found in heart tissue homogenates of LPS rats (8.0±0.6 vs. 26.4±4.8 pg/ml, (P<0.05). In conclusions, reduced HRV in HF band may indicate a decreased parasympathetic activity in LPS-induced endotoxemia as basic characteristics of altered cardiac control during response to endotoxemia.

An alternative approach to approximate entropy threshold value (r) selection: application to heart rate variability and systolic blood pressure variability under postural challenge

This study presents an alternative approach to approximate entropy (ApEn) threshold value (r) selection. There are two limitations of traditional ApEn algorithm: (1) the occurrence of undefined conditional probability (CPu) where no template match is found and (2) use of a crisp tolerance (radius) threshold 'r'. To overcome these limitations, CPu is substituted with optimum bias setting ɛ opt which is found by varying ɛ from (1/N - m) to 1 in the increments of 0.05, where N is the length of the series and m is the embedding dimension. Furthermore, an alternative approach for selection of r based on binning the distance values obtained by template matching to calculate ApEnbin is presented. It is observed that ApEnmax, ApEnchon and ApEnbin converge for ɛ opt = 0.6 in 50 realizations (n = 50) of random number series of N = 300. Similar analysis suggests ɛ opt = 0.65 and ɛ opt = 0.45 for 50 realizations each of fractional Brownian motion and MIX(P) series (Lu et al. in J Clin Monit Comput 22(1):23-29, 2008). ɛ opt = 0.5 is suggested for heart rate variability (HRV) and systolic blood pressure variability (SBPV) signals obtained from 50 young healthy subjects under supine and upright position. It is observed that (1) ApEnbin of HRV is lower than SBPV, (2) ApEnbin of HRV increases from supine to upright due to vagal inhibition and (3) ApEnbin of BPV decreases from supine to upright due to sympathetic activation. Moreover, merit of ApEnbin is that it provides an alternative to the cumbersome ApEnmax procedure.

Using Renyi entropy to detect early cardiac autonomic neuropathy

Cardiac Autonomic Neuropathy (CAN) is a disease that involves nerve damage leading to abnormal control of heart rate. CAN affects the correct operation of the heart and in turn leads to associated arrhythmias and heart attack. An open question is to what extent this condition is detectable by the measurement of Heart Rate Variability (HRV). An even more desirable option is to detect CAN in its early, preclinical stage, to improve treatment and outcomes. In previous work we have shown a difference in the Renyi spectrum between participants identified with well-defined CAN and controls. In this work we applied the multi-scale Renyi entropy for identification of early CAN in diabetes patients. Results suggest that Renyi entropy derived from a 20 minute, Lead-II ECG recording, forms a useful contribution to the detection of CAN even in the early stages of the disease. The positive α parameters (1 ≤ α ≤ 5) associated with the Renyi distribution indicated a significant difference (p < 0.00004) between controls and early CAN as well as definite CAN. This is a significant achievement given the simple nature of the information collected, and raises prospects of a simple screening test and improved outcomes of patients.

Complexity of heart rate variability in type 2 diabetes - effect of hyperglycemia

Heart rate variability (HRV) is reduced in diabetes mellitus (DM) patients, suggesting dysfunction of cardiac autonomic regulation which has been associated with increased risk for pathological cardiac events. In this paper, we examined changes in HRV complexity in association to blood glucose level (BGL) and duration of diabetes. Resting HRV and BGL measurements of 32 healthy controls and 54 type 2 DM (T2DM) patients were analyzed. HRV complexity was assessed using Shannon entropy, sample entropy (SampEn), multiscale entropy (MSE), and multiscale Renyi entropy. HRV complexity increased with hyperglycemia indicated by increases in Shannon entropy and MSE and decreases in Renyi entropy for negative orders. Diabetes duration was strongly associated with Renyi entropy which increased for positive orders and decreased for negative orders as a function of disease duration. Shannon entropy, SampEn and MSE did not correlate with disease duration.

Limits of permutation-based entropies in assessing complexity of short heart period variability

The study compares permutation-based and coarse-grained entropy approaches for the assessment of complexity of short heart period (HP) variability recordings. Shannon permutation entropy (SPE) and conditional permutation entropy (CPE) are computed as examples of permutation-based entropies, while the k-nearest neighbor conditional entropy (KNNCE) is calculated as an example of coarse-grained conditional entropy. SPE, CPE and KNNCE were applied to ad-hoc simulated autoregressive processes corrupted by increasing amounts of broad band noise and to real HP variability series recorded after complete vagal blockade obtained via administration of a high dose of atropine (AT) in nine healthy volunteers and during orthostatic challenge induced by 90° head-up tilt (T90) in 15 healthy individuals. Over the simulated series the performances of SPE and CPE degraded more rapidly with the amplitude of the superimposed broad band noise than those of KNNCE. Over real data KNNCE identified the expected decrease of the HP variability complexity both after AT and during T90. Conversely SPE and CPE detected the decrease of HP variability complexity solely during T90 as a likely result of the more favorable signal-to-noise ratio during T90 than after AT. Results derived from both simulations and real data indicated that permutation-based entropies had a larger susceptibility to broad band noise than KNNCE. We recommend caution in applying permutation-based entropies in presence of short HP variability series characterized by a low signal-to-noise ratio.

Toll-like receptor 9 plays a key role in the autonomic cardiac and baroreflex control of arterial pressure

The crosstalk between the immune and the autonomic nervous system may impact the cardiovascular function. Toll-like receptors are components of the innate immune system and play developmental and physiological roles. Toll-like receptor 9 (TLR9) is involved in the pathogenesis of cardiovascular diseases, such as hypertension and heart failure. Since such diseases are commonly accompanied by autonomic imbalance and lower baroreflex sensitivity, we hypothesized that TLR9 modulates cardiac autonomic and baroreflex control of arterial pressure (AP). Toll-like receptor 9 knockout (TLR9 KO) and wild-type (WT) mice were implanted with catheters into carotid artery and jugular vein and allowed to recover for 3 days. After basal recording of AP, mice received methyl-atropine or propranolol. AP and pulse interval (PI) variability were evaluated in the time and frequency domain (spectral analysis), as well as by multiscale entropy. Spontaneous baroreflex was studied by sequence technique. Behavioral and cardiovascular responses to fear-conditioning stress were also evaluated. AP was similar between groups, but TLR9 KO mice exhibited lower basal heart rate (HR). AP variability was not different, but PI variability was increased in TLR9 KO mice. The total entropy was higher in TLR9 KO mice. Moreover, baroreflex function was found higher in TLR9 KO mice. Atropine-induced tachycardia was increased in TLR9 KO mice, whereas the propranolol-induced bradycardia was similar to WT mice. TLR9 KO mice exhibit increased behavioral and decreased tachycardia responses to fear-conditioning stress. In conclusion, our findings suggest that TLR9 may negatively modulate cardiac vagal tone and baroreflex in mice.

Analysis of autonomic modulation after an acute session of resistance exercise at different intensities in chronic obstructive pulmonary disease patients

Purpose

Physical exercises are employed as part of the treatment of patients with chronic obstructive pulmonary disease (COPD); however information regarding cardiac autonomic modulation after an acute session of resistance exercise (RE) is unknown. The aim of this study was to evaluate the cardiac autonomic modulation, via heart rate variability after an acute session of RE applied at different intensities in COPD patients.

Patients and methods

Twelve COPD patients underwent an acute session of RE with an intensity of 60% and another of 90% of the one repetition maximum test. For analysis of autonomic modulation, heart rate was recorded beat-by-beat for 20 minutes at rest and after the training session. Heart rate variability indexes were obtained in the time and frequency domains for the assessment of autonomic modulation.

Results

Regardless of exercise intensity, RE acute sessions influenced the autonomic modulation when the recovery period was compared with the baseline. An increase in standard deviation of normal to normal RR intervals was observed throughout recovery time after the RE, as compared to baseline in both protocols: 60% and 90% of the one repetition maximum test. The spectral component of low frequency index (ms) was higher throughout recovery when compared to baseline in both protocols. The same was also observed in the spectral component of high frequency index (ms) for the protocols of 60% and 90%.

Conclusion

RE sessions impact on the autonomic modulation of COPD patients by promoting differences in the recovery period compared to baseline, regardless of the intensity of the exercise performed.