Term Heart Rate Variability Research Articles

Heart rate variability and vibration perception threshold in healthy young adults with parental history of type 2 diabetes mellitus

The aim of the study was to evaluate cardiac autonomic function and somatic sensation in healthy young adults with parental history of type 2 diabetes mellitus (T2DM). The study was conducted on 40 young adults with parental history of T2DM and 40 young adults without parental history of T2DM. The short term heart rate variability (HRV) and vibration perception threshold (VPT) were assessed in both groups. In time domain measures, standard deviation of all RR intervals (SDNN) [26.5 (22–33) vs 33.5 (25–37.75) ms, P = 0.014], the square root of the mean of the sum of the squares of differences between adjacent RR intervals (RMSSD) [25.85 (16.22–33.8) vs 30.9 (24.4–41.67) ms, P = 0.013], and percentage of consecutive RR intervals that differ by more than 50 ms (pNN50) [4.6 (1.1–13.77) vs 12.4 (2.8–26.82) %, P = 0.022] were significantly less in young adults with parental history of T2DM. In frequency domain measures, low frequency (LF) [115.5 (83.75–140.75) vs 141 (104.25–249.75) ms2, P = 0.021], high frequency (HF) [114.5 (74.5–179) vs 182.5 (104.25–247) ms2, P = 0.006] and HF [33.3 (24.52–53.22) vs 56.8 (43.02–69.17) nu, P = 0.002] were significantly less in young adults with parental history of T2DM. Whereas, LF [49.8 (36.97–69.55) vs 45.2 (35.4–57.02) nu] and LF/HF [0.98 (0.65–1.62) vs 0.85 (0.5–2.02) %] were comparable between the groups. VPT (for peripheral neuropathy) was comparable between the groups. This study indicates that parental type 2 diabetes has an impact on the cardiac autonomic function in non-diabetic young adults.

Heart rate variability in type 2 diabetics

AIM: To assess the autonomic function in type 2 diabetics by evaluating Heart Rate Variability. METHODS: The study was conducted in 80 diabetics and 80 healthy controls, to assess the autonomic control of the heart rate using short term Heart Rate Variability in respect of time domain and frequency domain parameters. RESULTS: The HRV values such as SDNN, RMSSD, NN50; pNN50; LF; HF, LF/HF were significantly (p<0.05) reduced in diabeticscompared to healthy controls. CONCLUSION: This demonstrates that the subclinical autonomic dysfunction may be in asymptomatic diabetics. Parasympathetic regulation appears to be more significantly affected in the early asymptomatic diabetics. However, this study does not correlate HRV changes with various degrees of glycemic control and different durations of diabetes. More prospectivestudies in large diabetic populations are required to validate this inference.

English

AIM: To assess ability of cardiac autonomic function to cope with the physiological challenge of standing in Type 2 Diabetics. METHODS: This study was conducted in 40 normal and 40 Type 2 Diabetics, using short term Heart Rate variability test. RESULTS: The change in HRV values between the two postures were significantly reduced in Diabetics. ΔSDNN; Δ RMSSD, ΔNN50; ΔpNN50; Δ LF; Δ HF; Δ LF/HF (p<0.05) compared to healthy controls. CONCLUSION: This study demonstrates the presence of subclinical cardiac autonomic dysfunction in asymptomatic diabetics. More prospective studies on a large scale are required to validate this inference.

A study of short term heart rate variability in dipping tobacco users

Background: WHO (2010) reported the rise in the number of smokeless tobacco users in India. Dipping tobacco is a form of smokeless tobacco being used in these region in various trade names. Nicotine in smoking tobacco is found to alter the cardiovascular autonomic functions. As the expected cardiovascular mortality due to tobacco use across the globe is very high, here we studied the effect of dipping tobacco on cardiovascular autonomic function using the short term heart rate variability (HRV) analysis. Methods: Five minutes Lead II ECG at rest is aquired from male dipping tobacco moderate users (n=30) and age matched controls (n=30). Frequency and time domain parameters were derived and analysed using the Kubios HRV analysis software. Blood pressure changes were also compared. Unpaired ‘t’ test was done using SPSS 17.0 and statistical significance was set at p&lt;0.05. Results: Significant changes in certain parameters indicated that there is a developing set back in the sympathetic control over the heart (LF, 383.8+115.8, 952.8+131.1, p&lt;0.01) and also an established increase in diastolic blood pressure (73.1+2.8, 65.48+1.5, p&lt;0.01). Conclusion: Dipping tobacco a form of smokeless tobacco is equally harmful like any smoked tobacco in altering the cardiovascular autonomic function. Thereby the increase in smokeless tobacco users in India or any country is going to add up more to the mortality rate due to tobacco related diseases in near future as estimated by WHO. An immediate measure to stop the production, sale and use of these smokeless tobacco products could curb this menace. Asian Journal of Medical Science, Volume-5(3) 2014: 91-94 http://dx.doi.org/10.3126/ajms.v5i3.9588

Characterizing the phenotypes of obstructive sleep apnea: Clinical, sleep, and autonomic features of obstructive sleep apnea with and without hypoxia

ObjectiveThe pathophysiological basis of obstructive sleep apnea (OSA) is not completely understood and likely varies among patients. In this regard, some patients with OSA do not exhibit hypoxemia. We aimed to analyze the clinical, sleep, and autonomic features of a group of patients with severe OSA without hypoxia (OSA−h) and compare to OSA patients with hypoxia (OSA+h) and controls. MethodsFifty-six patients with OSA−h, 64 patients with OSA+h, and 44 control subjects were studied. Clinical and sleep features were analyzed. Besides, time- and frequency-domain heart rate variability (HRV) measures comprising the mean R–R interval, the standard deviation of the RR intervals (SDNN), the low frequency (LF) oscillations, the high frequency (HF) oscillations, and the LF/HF ratio, were calculated across sleep stages during a one-night polysomnography. ResultsOSA−h patients had a lower body mass index, a lower waist circumference, lower apnea duration, and a higher frequency of previous naso-pharyngeal surgery when compared to OSA+h patients. In terms of heart rate variability, OSA+h had increased LF oscillations (i.e., baroreflex function) during N1–N2 and rapid eye movement (REM) sleep when compared to OSA−h and controls. Both OSA+h and OSA−h groups had decreased HF oscillations (i.e., vagal inputs) during N1–N2, N3 and REM sleep when compared to controls. The LF/HF ratio was increased during N1–N2 and REM sleep, only in patients with OSA+h. ConclusionsPatients with OSA−h exhibit distinctive clinical, sleep, and autonomic features when compared to OSA with hypoxia. SignificanceOSA is a heterogeneous entity. These differences must be taken into account in future studies when analyzing therapeutic approaches for sleep apnea patients.

Wet Cupping Therapy Restores Sympathovagal Imbalances in Cardiac Rhythm

A recent study showed that cupping had therapeutic effects in rats with myocardial infarction and cardiac arrhythmias. The current studyaimed to investigate the possible useful effects of cupping therapy on cardiac rhythm in terms of heart rate variability (HRV). Forty healthy participants were included. Classic wet cupping therapy was applied on five points of the back. Recording electrocardiography (to determine HRV) was applied 1 hour before and 1 hour after cupping therapy. All HRV parameters increased after cupping therapy compared with before cupping therapy in healthy persons. These results indicate for the first time in humans that cupping might be cardioprotective. In this study, cupping therapy restored sympathovagal imbalances by stimulating the peripheral nervous system.

Assessment of cardiac autonomic function in patients with Duchenne muscular dystrophy using short term heart rate variability measures

BackgroundDuchenne muscular dystrophy (DMD) is a hereditary neuromuscular disorder frequently associated with progressive cardiac dysfunction, and is one of the common causes of death in these children. Early diagnostic markers of cardiac involvement might help in timely intervention. In this study we compared the short term HRV measures of DMD children with that of healthy subjects. MethodOne hundred and twenty-four genetically confirmed boys with DMD and 50 age matched controls were recruited. Error-free, electrocardiogram was recorded in all subjects at rest in the supine position. HRV parameters were computed in time and frequency domains. Time domain measures included standard deviation of NN interval (SDNN), and root of square mean of successive NN interval (RMSSD). Frequency domain consisted of total, low frequency and high frequency power values. Ratio of low frequency and high frequency power values (LF/HF) was determined using customized software. ResultsHRV parameters were significantly altered in DMD children as compared to healthy controls. Following parameters [mean (SD)] were reduced in DMD as compared to controls; RMSSD (in ms) [52.14 (33.2) vs 64.64 (43.2); p = 0.038], High frequency component (nu) [38.77 (14.4) vs 48.02 (17.1); p = 0.001] suggesting a loss of vagal tone. In contrast, measure of sympathovagal balance LF/HF [1.18 (0.87) vs 0.89 (0.79); p = 0.020] was increased in DMD group. ConclusionIn this cross sectional study we have demonstrated alteration in autonomic tone in DMD. Loss of vagal tone and an increase in sympathetic tone were observed in DMD children. Further prospective studies are required to confirm the utility of these measures as predictors of adverse cardiac outcome in DMD.

Influence of resistance training on cardiac autonomic modulation: literature review

Among the benefits provided by its influence on cardiac modulation draws attention, given the importance of the nervous in the control of the internal functions of the body. The objective of this narrative review was to gather information about the effects of training on modulation in healthy young adults, assessed through rate This search was performed in May 2013 in the following databases: Medical Literature Analysis and Retrieval System Online (MEDLINE/PubMed), Physiotherapy Evidence Database (PEDro), Scientific Electronic Library Online (SciELO) and Literature and Latin American and Caribbean Health Sciences (LILACS), with articles published between January 2003 and March 2013. The keywords used were: resistance training, lifting, exercise therapy crossreferenced with autonomic nervous system and parasympathetic nervous system and also the term heart rate variability. Of the 1,940 titles found, 3 complete articles were selected to compose the review. Data were analyzed qualitatively. The analysis of these studies suggests that training can change the rate variability indices in the population of healthy young adults and that this change is due to increased vagal activity, provided by the practice of this type of training.

Short term heart rate variability for early assessment of autonomic neuropathy in patients with type 2 diabetes mellitus: A comparative cross-sectional study

Background: Cardiovascular autonomic diabetic neuropathy (CADN) is an important diabetes-associated complication. Reduced heart rate variation is the earliest indicator of CADN. Aims: The aim was to study the prospect of using short term heart rate variability (HRV) analysis for early diagnosis of CADN. Settings and Design: The type of this study was hospital-based comparative cross-sectional study. Materials and Methods: A total of 30 asymptomatic, type 2 diabetic male patients with duration of diabetes of 1-5 years with random blood sugar ≥200 mg/dl (11.1 mmol/L) or fasting blood sugar ≥126 mg/dl (7 mmol/L) in the age group of 30-65 years were selected as subjects. Thirty age and sex matched healthy nondiabetics were selected as controls. HRV analysis was performed using electrocardiogram recorded in lead II, at rest, in the supine position for 5 min. Results: Total power, low frequency power, high frequency power, standard deviation of all R-R intervals, root mean square of successive RR-interval differences, number of intervals differing by >50 ms from adjacent interval (NN50), and percentage of NN50 (pNN50) were significantly less in diabetics when compared to nondiabetics ( P Conclusion: Data from the study demonstrated that asymptomatic diabetics with <5 years history had already developed autonomic neuropathy. Short term analysis of HRV can be used as a valuable tool for early diagnosis of autonomic neuropathy.

Heart Rate Variability in overweight individuals

Aim:To assess the influence of Body Mass Index(BMI)on autonomic function using short term Heart Rate Variability(HRV) test. Method: This study was conducted in 40normal weight individuals and40overweight individuals, to assess the influence of BMI on autonomic function as measured by the time domain and frequency domain parameters of short term HRV test. Results: The HRV values were significantly reduced in overweight individuals. ( SDNN p<0.05; RMSSD ,p<0.01 ;NN50 p<0.01 ; pNN50 ,p< 0.01 ; LF, p<0.05 ; HF ,p<0.01; LF/HF , p<0.01) , compared to healthy controls. Conclusion: This study demonstrates the possibility of autonomic dysfunction in asymptomatic overweight individuals. Limitation in the present study is that the correlation of HRV with visceral fat and the influence of ethnicity on BMI were not studied. More prospective studies in this regard are required to validate this inference.

Immediate effect of Different Pranayam on Short Term Heart Rate Variability in Health Care Students - A Preliminary Study

Introduction: Yoga produces consistent physiological changes and have sound scientific basis. Heart rate variability (HRV) has come to be widely used as a non-invasive tool to assess autonomic function in physiological as well as disease states. In view of this, the present study was aimed to study the effect of suryanadi(SNP) and chandranadi pranayams(CNP) on HRV in healthy young volunteers. Methodology: 11male volunteers aged between 20-30yrs were assigned to a sequence randomly. Each volunteer was taught both SNP and CNP by trained yoga teacher and made to practice under direct supervision between 4-6.30 pm in ACYTER lab, JIPMER. HRV was recorded by using BIOHARNESS AcqKnowledge 4.1 version and analyzed by Kubios HRV 2.00 software. Basal resting parameters and HRV were recorded for 5 minutes after that SNP was performed in six cycles per minute for 5 minutes followed by 5 min rest. Three such sessions (before, during and after) HRV were recorded. The same procedure and recording ware followed for CNP. Results: The time domain analysis of SNP revealed an increased heart rate with a decreased RMSSD, However the SDNN was increased. Frequency domain analysis, increased LF power and decreased HF power and LF/HF ratio increased after the intervention. In CNP, the time domain analysis, showed decreased heart rate and an increased pNN50. The frequency domain analysis revealed an increased HF power with decreased LF/HF ratio. Discussion: SNP increase the sympathetic activity and CNP increases the parasympathetic activity and these can be appropriately advocated in many chronic cardiovascular diseases where the autonomic imbalance is one of the primary derangements.

Relevance of hypoxia in sleep disordered breathing: distinct clinical, sleep, and autonomic features in obstructive sleep apnea with and without hypoxia

Introduction Some patients with obstructive sleep apnea do not exhibit hypoxemia. However, there is a lack of studies characterizing this subgroup of patients. We aimed to analyze the clinical, sleep, and autonomic features of a group of patients with obstructive sleep apnea without hypoxia (OSAwh). Materials and methods Twenty-eight patients with OSAwh, 32 patients with OSA with hypoxia, and 22 control subjects were studied. Patient groups were matched in matched in age and gender. Clinical and sleep features were analyzed. Besides, time- and frequency-domain heart rate variability (HRV) measures (mean R–R interval, SDNN, LF oscillations, HF oscillations, and the LF/HF ratio) were calculated across all sleep stages during a one- night polysomnography. Results We found that OSAwh patients had a lower body mass index, a lower waist circumference, and a higher frequency of previous naso-pharyngeal surgery when compared to OSA with hypoxia patients. In terms of heart rate variability, OSA had increased LF oscillations (i.e., baroreflex function) during N1-N2 and REM sleep when compared to OSAwh and controls. OSA and OSAwh had decreased HF oscillations (i.e., vagal inputs) during N1-N2, N3 and REM sleep when compared to controls. The LF/HF ratio was increased during N1-N2 and REM sleep, only in patients with OSA. Conclusion In conclusion, patients with OSAwh exhibit distinctive clinical, sleep, and autonomic features when compared to obstructive sleep apnea with hypoxia. These differences must be taken into account in future studies when analyzing therapeutic approaches for sleep apnea patients. Acknowledgements Clinica Universidad de Navarra.

Abstract 9446: Periodic Acceleration (pGz) Preserves Heart Rate Variability After Cardiac Arrest

Heart Rate Variability (HRV) and lack thereof is important in predicting neurological outcome after brain injury and cardiac arrest (CA). The post CA period has decreased HRV compared to pre-arrest, and interventions such hypothermia may preserve HRV. Periodic acceleration (pGz) the sinusoidal head to foot motion of the supine body with a motion platform increases endothelial derived NO (eNO) and neuronal nitric oxide (nNOS) in heart via pulsatile shear stress, and preconditions from global and focal ischemia reperfusion injury (I/R). NO has a facilitator role on vagal chronotropism and inhibition of nNOS reduces HRV. CA is a model of whole body I/R. This study tests whether pGz applied prior to whole body I/R preserves HRV in a pig model of CA. Fourteen male swine (40-50lbs) were anesthetized, intubated and instrumented to measure EKG and hemodynamics. They were placed on a motion platform and randomized to 1 hr of pGz (180 cpm and Gz ± 0.4) or none control (C). VF was electrically induced and unsupported for 8 min, followed by chest compression and defibrillation until return of spontaneous circulation (ROSC) or 10 min. Short term HRV (excluding ectopic beats) was measured including; time domain; SDNN (standard deviation of normal to normal intervals), RMSSD (square root of the mean squared differences of successive normal to normal intervals) and frequency domain, at baseline (BL1), after pGz or C (BL2) and 30, to 180 mins after ROSC (ROSC30, 60,120,180 ). All animals had ROSC after a median of 4 defibrillation attempts. There were no differences between groups in defibrillation attempts, time to ROSC, arterial blood gases or hemodynamics over time. pGz animals had less hemodynamically significant arrhythmias in the first 30 mins ROSC; C (35) vs pGz (7) (p&lt; 0.05) Data mean ±SEM * p &lt; 0.05 C vs pGz. pGz applied prior to I/R in addition to being cardioprotective preserves HRV and is a novel non invasive method to preserve autonomic nervous system output and precondition the myocardium.

Exercise-induced exaggerated blood pressure response in men with the metabolic syndrome

Abnormalities in the autonomic nervous system in the presence of insulin resistance may be involved in the pathophysiology of obesity-associated hypertension. We evaluated the association between exercise-induced exaggerated blood pressure (BP) response [exercise-induced hypertension (EIH)] and heart rate variability (HRV) and insulin resistance in men with metabolic syndrome (MetS). Ninety-eight resting normotensive men with MetS underwent a maximal symptom-limited treadmill test. BP was measured after a 5-min rest (anticipatory BP) every 3 min during exercise and during the recovery period. EIH was defined as maximum systolic BP of 220 mmHg or higher and/or maximum diastolic BP of 100 mmHg or higher. Insulin resistance was estimated using HOMA-IR. Each participant underwent a 3-h oral glucose tolerance test (OGTT). HRV was derived from a 24-h Holter. About half of the participants (52%) presented EIH. Resting BPs at baseline were 125 ± 10/83 ± 7 mmHg in participants with EIH and 120 ± 9/80 ± 6 mmHg (P = 0.01) in the group with normal BP response to exercise. OGTT glucose levels were higher in the group with EIH (all P < 0.04) as well as HOMA-IR compared with participants with normal BP response to exercise (P = 0.03). In terms of HRV, 24-h standard deviation of the RR intervals (SDNN) was lower in participants with EIH (P = 0.04) as well as 24-h daytime and night-time high frequency and low frequency (P < 0.05). Normotensive men with MetS but with EIH have greater insulin resistance as well as lower HRV parasympathetic and sympathetic indices. These features may be involved in the pathophysiology of EIH.

Comparison between standard short-term, very-short and ultra-short-term heart rate variability analysis in healthy subjects during exercise testing

Purpose: Stepwise exercise testing, with workload increasing every two minutes, determines progressive changes of autonomic nervous system (ANS) adaptations that might affect stationarity during the intervals sampled for Standard Short term (SST) heart rate variability analysis (HRVa) according to ESC guidelines. To quantify such transients short-lasting changes very-short (VST) and ultra-short term (UST) analysis (HRVa) might be more appropriate, but have not been validated so far. The study aimed to evaluate, in healthy subjects (HS), the information provided by VST and UST HRVa in comparison with SSTHRVa, during exercise testing. Methods: 12-lead ECG recorded in 25 HS, before, at each workload step and during recovery after maximal exercise testing with a bicycle ergometer (workload increasing by 25 watts, every two minutes). The coefficients of variation (CV) and of reproducibility (CR) and the Pearson correlation (R2) of the same linear [time-domain (TD) and frequency-domain (FD)] and nonlinear (NL) HRV parameters, computed from SST (5-minutes), VST (60-seconds) and UST (30-seconds) intervals with Kubios HRV 2.1, (Biosignal Analysis and Medical Imaging Group, Department of Physics, University of Kuopio, Finland), were evaluated. VST and UST intervals were selected at the end of each step. Results: SSTHRVa was appropriate only at rest, before exercise and in the recovery phase. A significant correlation was found among parameters estimated from 5-minutes, 60- and 30-seconds time intervals (Pearson R2 range: 0.42 -0.98, p< 0.05 – 0.000001), except for a certain degree of variability of frequency peaks estimated with the Fast Fourier transform. At peak workload only VST and USTHRVa parameters were significantly correlated (R2 range: 0.6 -0.99, p< 0.01 – 0.000001), whereas the 5-minute interval was too long and averaged ther consecutive workload steps. At peak exercise, the CV and the CR of LF/HF, estimated from 60- and 30-seconds time-intervals were 9.3 and 0.54, respectively, thus almost optimal. Among NL methods, only the Poincare plot was calculable in all conditions. Instead recurrence plot, detrended fluctuation, approximate entropy, sample entropy and correlation dimension parameters required at least 40-seconds intervals, unless heart rate exceeded 100 bpm. Discussion: The good correlation among HRV parameters estimated at rest from 5-minutes, 60- and 30-seconds time intervals proves that VST or UST HRVa are a feasible option to evaluate changes of cardiac autonomic modulation induced by stepwise exercise testing when time-variant HRVa is unavailable.

Heart rate variability and vibration perception threshold in type 2 diabetes mellitus

The aim of the study was to compare heart rate variability (HRV) and vibration perception threshold (VPT) of patients with type 2 diabetes mellitus (T2DM) with control. The study was conducted on 60 patients with T2DM and 30 controls. The short term HRV and VPT were assessed in the both groups. All the time domain measures, SDNN [26 (15.5–35) vs 36 (30–40.25) ms, P = 0.002], RMSSD [25.9 (11.95–40.45) vs 36.65 (27.05–44.13) ms, P = 0.002], and pNN50 [3.5 (0.23–21.83) vs 16.4 (4.45–27.63) %, P = 0.002] were less in T2DM. Similarly, in frequency domain, low frequency (LF) power [81 (32–148.75) vs 126 (85.25–237.75) ms2, P = 0.007], high frequency (HF) power [81 (16.75–187.75) vs 182.5 (121.50–281.75) ms2, P = 0.001] and HF nu [54.5 (33.2–63.83) vs 59.7 (50–75.05), P = 0.03] were significantly less in T2DM. Whereas, LF nu [45.25 (35.28–63.93) vs 44.65 (33.7–65.35)] and LF/HF ratio [0.78 (0.54–1.13) vs 0.7 (0.4–1)] were comparable between the groups. In Poincare plot, SD1 [18.5 (8.73–28.98) vs 26.2 (19.33–31.65) ms, P = 0.003] and SD2 [37 (26.13–51.18) vs 48.6 (39.63–56.15) ms, P = 0.016] as well as the ratio of SD1/SD2 [0.18 (0.14–0.21) vs 0.29 (0.25–0.33) P = 0.001] were less in T2DM. However, VPT was comparable between the groups. The patients with T2DM had reduced parasympathetic activity whereas sympathetic activity and vibration (somatic) sensation were similar. It indicates that cardiac parasympathetic activity in T2DM is affected before sympathetic and somatic activity.

Heart Rate or Heart Rate Variability? Evidence from Breath Suspension

Studies have assessed changesin autonomic variables in terms of heart rate variability (HRV) during meditation, but their results have been conflicting. Recently, Telles and colleagues’ excellent study 1 compared changes in HRV measures during the first four meditative states—cancalata (random thinking), ekagrata (nonmeditative focusing), dharana (meditative focusing), and dhyana (effortless meditation)— described in traditional yoga texts. Their results revealed that the discrepancy in changes in HRV measures might be due to the different stages. At the end of the Discussion section, the authors mention the ultimate state in yoga, samadhi, which manifests with breath suspension. They did not explore this advancedmeditativestateintheirstudy.Therefore,wewould like to share our results about changes in HRV measures during breath suspension. We collected and compared HRV measures in time and frequency domains, along with heart rate and blood pressure, during random thinking, breath meditation, and the advanced meditative stage with breath suspension in 10 meditative sessions conducted by an experienced meditator. The results (Table 1) show that none of HRV measures of time and frequency domains differed significantly during meditation. In contrast, heart rate and systolic blood pressure reliably decreased as meditation advanced. Because the meditation technique used in this study is close to meditative focusing, the results are concordant with the dharana state described in Telles and colleagues’ study. By using a device to monitor the chest circumstance of the meditator, the current study confirmed that the timing of breath suspension coincided with the subjective experience of breath suspension, complete quiescence, and absence of self-boundary. HRV measures, on the other hand, did not concord with the subjective experience of pure quiescence. Because other authors have argued that HRV is not an index of autonomic control of the circulation, 2 whether HRV can be an ideal index of sympathetic or parasympathetic activities remains questionable. According to the current results, the variations of HRV measures, manifested as the ratio of standard deviation to mean, are much greater than those of heart rate and blood pressure. This has also been shown in previous studies. 1,3,4 According to this view, HRV measures cannot be accurate indexes. The current results suggest that heart rate itself is a better HRV measure for meditation and agree with findings from Mukherjee et al. 5 These authors examined the sensitivity to mental effort and reliability of various HRV measures. Their

Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation.

Highlights in clinical autonomic neurosciences: Emerging autonomic diagnostics for cardiovascular disorders

This clinical update will focus on articles about diagnostics for cardiovascular disorders. The first article uses a traditional autonomic technique, short term heart rate variability, to predict which patients going for diagnostic coronary angiography will have significant obstructive coronary artery disease. The second article involves early work on a novel method to measure a surrogate marker for hemodynamic collapse.

Why should one normalize heart rate variability with respect to average heart rate

Heart rate variability (HRV) is a recognized risk factor in many disease states (Bravi et al., 2011; Sacha et al., 2013a). However, HRV is significantly correlated with an average heart rate (HR), and this association is both physiologically and mathematically determined. The physiological determination comes from the autonomic nervous system activity (Task Force of the European Society of Cardiology, and the North American Society of Pacing and Electrophysiology, 1996), but the mathematical one is caused by the non-linear (inverse) relationship between R-R interval and HR (Sacha and Pluta, 2005a,b, 2008). HRV may be estimated by using R-R interval (the most frequent method) or HR signals, yet, they both do not yield the same results since they are inversely related with each other—indeed, the analyses are mathematically biased (Sacha and Pluta, 2005a,b). If one uses R-R intervals, the same changes of HR cause much higher fluctuations of R-R intervals for the slow average HR than for the fast one (Figure ​(Figure1A).1A). Conversely, if one employs HR signals, the same changes of R-R intervals cause much higher fluctuations of HR for the fast than slow average HR (Figure ​(Figure1B).1B). Consequently, due to these mathematical reasons, HRV estimated from R-R intervals should negatively correlate with average HR (or positively with average R-R interval), but HRV estimated from HR signals should be positively correlated with average HR (or negatively with average R-R interval) (Sacha and Pluta, 2005a,b). Moreover, due to the inverse relationship between R-R interval and HR, there is a possibility that a given patient may have higher HRV than another in terms of R-R intervals and lower HRV in terms of HRs—Figure 1C explains such a case (Sacha and Pluta, 2005a). Figure 1 (A)The non-linear (mathematical) relationship between R-R interval and heart rate is depicted. One can see that the oscillations of a slow average heart rate (x-axis, dark gray area) result in much greater oscillations of RR intervals (y-axis, dark gray ... Another mathematical problem concerning the association between HRV and HR is presented in Figure ​Figure1D.1D. One can see that the fluctuations of R-R intervals may be potentially very high for slow average HR, however, the same fluctuations are not possible for fast average HR, since the R-R intervals should have become negative. The same problem can be met if one calculates HRV from HR signals, i.e., the average HR of 80 bpm may potentially fluctuate between 30 and 130 bpm (i.e., the fluctuation amplitude equals 100 bpm), however, such fluctuations are not possible for the average HR of 40 bpm, since the heart rhythm must have fluctuated between −10 and 90 bpm. Due to the above facts, the standard HRV analysis is mathematically biased, particularly if patients differ in terms of their average HRs. The only way to overcome it is to calculate HRV with respect to the average value, i.e., to normalize the fluctuations with respect to the mean (Sacha and Pluta, 2005a,b, 2008). One can do that by division of the signal by the average R-R interval in the case of R-R interval signal, or by the average HR in the case of HR signal. Moreover, this way the same results are obtained no matter if one calculates HRV from R-R intervals or HRs (Sacha and Pluta, 2005a). Such an approach enables to explore the HR contribution to the physiological and clinical significance of HRV (Billman, 2013; Sacha et al., 2013a). Recently, this approach has been further developed to enhance or completely remove the HR influence (even physiological one) on HRV, what turned out to provide valuable information on cardiac and non-cardiac prognosis in patients after myocardial infarction—the details have been published elsewhere (Sacha et al., 2013a,b,c). To conclude, HRV is significantly associated with HR, which is caused by both physiological and mathematical phenomena, however, by a simple mathematical modification one may exclude mathematical bias and explore a real clinical value of HR and its variability.