Quantification Of Heart Rate Variability Research Articles

Characterisation of neonatal cardiac dynamics using ordinal partition network.

The maturation of the autonomic nervous system (ANS) starts in the gestation period and it is completed after birth in a variable time, reaching its peak in adulthood. However, the development of ANS maturation is not entirely understood in newborns. Clinically, the ANS condition is evaluated with monitoring of gestational age, Apgar score, heart rate, and by quantification of heart rate variability using linear methods. Few researchers have addressed this problem from the perspective nonlinear data analysis. This paper proposes a new data-driven methodology using nonlinear time series analysis, based on complex networks, to classify ANS conditions in newborns. We map 74 time series given by RR intervals from premature and full-term newborns to ordinal partition networks and use complexity quantifiers to discriminate the dynamical process present in both conditions. We obtain three complexity quantifiers (permutation, conditional, and global node entropies) using network mappings from forward and reverse directions, and considering different time lags and embedding dimensions. The results indicate that time asymmetry is present in the data of both groups and the complexity quantifiers can differentiate the groups analysed. We show that the conditional and global node entropies are sensitive for detecting subtle differences between the neonates, particularly for small embedding dimensions (m < 7). This study reinforces the assessment of nonlinear techniques for RR interval time series analysis. Graphical Abstract.

Association of Short-term Heart Rate Variability and Sudden Unexpected Death in Epilepsy.

We compared heart rate variability (HRV) in sudden unexpected death in epilepsy (SUDEP) cases and living epilepsy controls. This international, multicenter, retrospective, nested case-control study examined patients admitted for video-EEG monitoring (VEM) between January 1, 2003, and December 31, 2014, and subsequently died of SUDEP. Time domain and frequency domain components were extracted from 5-minute interictal ECG recordings during sleep and wakefulness from SUDEP cases and controls. We identified 31 SUDEP cases and 56 controls. Normalized low-frequency power (LFP) during wakefulness was lower in SUDEP cases (median 42.5, interquartile range [IQR] 32.6-52.6) than epilepsy controls (55.5, IQR 40.7-68.9; p = 0.015, critical value = 0.025). In the multivariable model, normalized LFP was lower in SUDEP cases compared to controls (contrast -11.01, 95% confidence interval [CI] -20.29 to 1.73; p = 0.020, critical value = 0.025). There was a negative correlation between LFP and the latency to SUDEP, where each 1% incremental reduction in normalized LFP conferred a 2.7% decrease in the latency to SUDEP (95% CI 0.95-0.995; p = 0.017, critical value = 0.025). Increased survival duration from VEM to SUDEP was associated with higher normalized high-frequency power (HFP; p = 0.002, critical value = 0.025). The survival model with normalized LFP was associated with SUDEP (c statistic 0.66, 95% CI 0.55-0.77), which nonsignificantly increased with the addition of normalized HFP (c statistic 0.70, 95% CI 0.59-0.81; p = 0.209). Reduced short-term LFP, which is a validated biomarker for sudden death, was associated with SUDEP. Increased HFP was associated with longer survival and may be cardioprotective in SUDEP. HRV quantification may help stratify individual SUDEP risk. This study provides Class III evidence that in patients with epilepsy, some measures of HRV are associated with SUDEP.

Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP.

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP’s PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via HR and HRV were assessed via WHOOP 2.0 and ECG over 15 opportunities during October–December 2018. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP’s proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10–11%) and SWC (5–5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP’s proprietary filter, which approached or exceeded the CV (3–13%) and SWC (1.5–6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.

Impact of Using Different Levels of Threshold-Based Artefact Correction on the Quantification of Heart Rate Variability in Three Independent Human Cohorts.

Heart rate variability (HRV) is a non-invasive indicator of autonomic nervous system function. HRV recordings show artefacts due to technical and/or biological issues. The Kubios software is one of the most used software to process HRV recordings, offering different levels of threshold-based artefact correction (i.e., Kubios filters). The aim of the study was to analyze the impact of different Kubios filters on the quantification of HRV derived parameters from short-term recordings in three independent human cohorts. A total of 312 participants were included: 107 children with overweight/obesity (10.0 ± 1.1 years, 58% men), 132 young adults (22.2 ± 2.2 years, 33% men) and 73 middle-aged adults (53.6 ± 5.2 years, 48% men). HRV was assessed using a heart rate monitor during 10–15 min, and the Kubios software was used for HRV data processing using all the Kubios filters available (i.e., 6). Repeated-measures analysis of variance indicated significant differences in HRV derived parameters in the time-domain (all p < 0.001) across the Kubios filters in all cohorts, moreover similar results were observed in the frequency-domain. When comparing two extreme Kubios filters, these statistical differences could be clinically relevant, e.g. more than 10 ms in the standard deviation of all normal R-R intervals (SDNN). In conclusion, the results of the present study suggest that the application of different Kubios filters had a significant impact on HRV derived parameters obtained from short-term recordings in both time and frequency-domains.

Multiscale Distribution Entropy Analysis of Heart Rate Variability Using Differential Inter-Beat Intervals

Heart rate variability (HRV) is a widely used measure for the variation of the heartbeat intervals controlled by the autonomic nervous system (ANS), which is primarily obtained by electrocardiogram (ECG) signals. In general, HRV of a healthy person exhibits long-range correlations with dynamic fluctuations, whether the complexity of HRV decreases with aging and incidence of disease. Recently, the complexity of differential inter-beat intervals, referred to differential R-R intervals, is known to be more effective than original R-R intervals to reflect HRV. The multiscale based entropy methods have been developed to quantify HRV using R-R intervals. In spite of their capability, it still remains unreliable quantification of HRV. Here, we propose a new multiscale complexity quantification measure with differential R-R intervals for HRV analysis. To verify the performance of the proposed method, we evaluate the complexity of differential HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The results show that multiscale distribution entropy (MDE) of differential R-R interval has improved capability for quantifying the complexity of HRV regardless of the length of time series.

Differences in Daytime vs. Night-time Travel Stress and Recovery as Measured by Heart Rate Variability

PURPOSE: Air travel over several time zones causes disruption of circadian rhythms resulting in disorientation, nausea, gastrointestinal distress and difficulty concentrating. This may compromise health and decrease performance in travelling athletes. Heart rate variability (HRV) used to assess autonomic cardiac control may be an indicator of jetlag. This case study investigated trans-meridian travelling effects on the autonomic nervous system (ANS) by measuring time domain (RMSSD), high frequency domain (HF) and autonomic balance (LF/HF) indicators. METHODS: A recreational athlete was monitored on tour from South Africa (SA) to New Zealand (NZ) for 28 days. HRV measurements were averaged over 3 days/nights and reported as means (SD): a) after a 18 hour flight from SA, b) after 1 week in NZ, c) after 2 weeks in NZ, d) upon return to SA. RESULTS: Active daytime vagal cardiac influence showed a return to normal/higher values measured after 2 weeks of arrival in Auckland, with a marked decline directly upon return to SA; RMSSD (ms): a) 45.63 (23.88), b) 62.90 (32.20), c) 67.00 (40.06) and upon return to SA d) 43.50 (25.75); HF (ms2): a) 613.33 (366.11), b) 1164.00 (632.90), c) 1324.50 (795.30) and upon return to SA d) 834.33 (849.58). However, night time measurements indicated a decrease of the vagal control set point. RMSSD (ms): a) 52.37 (26.93), b) 50.55 (29.29), c) 46.80 (27.02) and upon return to SA d) 36.07 (18.12); HF (ms2): a) 829.33 (471.39), b) 772.50 (451.86), c) 530.00 (309.22) and upon return to SA d) 336.00 (179.77). Daytime autonomic balance (LF/HF) followed the same pattern, returning to lower/normal values after 2 weeks in NZ, and upon homecoming values reversed, signalling a new stress response due to the return flight: a) 3.66 (1.97), b) 2.83 (1.66), c) 2.71 (1.59) and finally d) 7.64 (4.50). Night time LF/HF values had small increases during the first 2 weeks with a stress response upon return to SA: a) 2.81 (1.62), b) 2.83 (1.66), c) 3.25 (1.88) and d) 6.40 (3.39). CONCLUSION: Reduced daytime vagal control improved with time (i.e. ANS adaptation) following international travel, illustrating the potential for HRV quantification as a jetlag marker. A novel finding is that during periods of travel it appears if autonomic cardiac control, as measured by HRV during daytime, differs to the nocturnal response.

Non-invasive real-time autonomic function characterization during surgery via continuous Poincar\xe9 quantification of heart rate variability

Heart rate variability (HRV) provides an excellent proxy for monitoring of autonomic function, but the clinical utility of such characterization has not been investigated. In a clinical setting, the baseline autonomic function can reflect ability to adapt to stressors such as anesthesia. No monitoring tool has yet been developed that is able to track changes in HRV in real time. This study is a proof-of-concept for a non-invasive, real-time monitoring model for autonomic function via continuous Poincaré quantification of HRV dynamics. Anonymized heart rate data of 18 healthy individuals (18–45 years) undergoing minor procedures and 18 healthy controls (21–35 years) were analyzed. Patients underwent propofol and fentanyl anesthesia, and controls were at rest. Continuous heart rate monitoring was carried out from before aesthetic induction to the end of the surgical procedure. HRV components (sympathetic and parasympathetic) were extracted and analyzed using Poincaré quantification, and a real-time assessment tool was developed. In the patient group, a significant decrease in the sympathetic and parasympathetic components of HRV was observed following anesthesia (SD1: p = 0.019; SD2: p = 0.00027). No corresponding change in HRV was observed in controls. HRV parameters were modelled into a real-time graph. Using the monitoring technique developed, autonomic changes could be successfully visualized in real-time. This could provide the basis for a novel, fast and non-invasive method of autonomic assessment that can be delivered at the point of care.

Which heart rate variability index is an independent predictor of mortality in cirrhosis?

BackgroundLiver cirrhosis is associated with reduced heart rate variability (HRV), which indicates impaired integrity of cardiovascular control in this patient population. There are several different indices for HRV quantification. The present study was designed to: 1) determine which of the HRV indices is best at predicting mortality in patients with cirrhosis; 2) verify if such ability to predict mortality is independent of the severity of hepatic failure. MethodsTen minutes electrocardiogram was recorded in 74 patients with cirrhosis. Heart rate fluctuations were quantified using statistical, geometrical and non-linear analysis. The patients were followed-up for 18months and information was collected on the occurrence of death/liver transplantation. ResultsDuring the follow-up period, 24 patients (32%) died or were transplanted for hepatic decompensation. Cox’s regression analysis showed that SDNN (total HRV), cSDNN (corrected SDNN), SD1 (short-term HRV), SD2 (long-terms HRV) and spectral indices could predict survival in these patients. However, only SD2 and cSDNN were shown to be independent of MELD in predicting survival. The prognostic value of HRV indices was independent of age, gender, use of beta blockers, and the aetiology of liver disease. ConclusionTwo HRV indices were identified that could predict mortality in patients with cirrhosis, independently of MELD. These indices are potentially useful tools for survival prediction.

Paroxysmal atrial fibrillation prediction method with shorter HRV sequences

This paper proposes a method that predicts the onset of paroxysmal atrial fibrillation (PAF), using heart rate variability (HRV) segments that are shorter than those applied in existing methods, while maintaining good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to stabilize (electrically) and prevent the onset of atrial arrhythmias with different pacing techniques. We investigate the effect of HRV features extracted from different lengths of HRV segments prior to PAF onset with the proposed PAF prediction method. The pre-processing stage of the predictor includes QRS detection, HRV quantification and ectopic beat correction. Time-domain, frequency-domain, non-linear and bispectrum features are then extracted from the quantified HRV. In the feature selection, the HRV feature set and classifier parameters are optimized simultaneously using an optimization procedure based on genetic algorithm (GA). Both full feature set and statistically significant feature subset are optimized by GA respectively. For the statistically significant feature subset, Mann–Whitney U test is used to filter non-statistical significance features that cannot pass the statistical test at 20% significant level. The final stage of our predictor is the classifier that is based on support vector machine (SVM). A 10-fold cross-validation is applied in performance evaluation, and the proposed method achieves 79.3% prediction accuracy using 15-minutes HRV segment. This accuracy is comparable to that achieved by existing methods that use 30-minutes HRV segments, most of which achieves accuracy of around 80%. More importantly, our method significantly outperforms those that applied segments shorter than 30 minutes.

Pannexin-1 Deficient Mice Have an Increased Susceptibility for Atrial Fibrillation and Show a QT-Prolongation Phenotype

Background/Aims: Pannexin-1 (Panx1) is an ATP release channel that is ubiquitously expressed and coupled to several ligand-gated receptors. In isolated cardiac myocytes, Panx1 forms large conductance channels that can be activated by Ca²⁺ release from the sarcoplasmic reticulum. Here we characterized the electrophysiological function of these channels in the heart in vivo, taking recourse to mice with Panx1 ablation. Methods: Cardiac phenotyping of Panx1 knock-out mice (Panx1^-/-) was performed by employing a molecular, cellular and functional approach, including echocardiography, surface and telemetric ECG recordings with QT analysis, physical stress testing and quantification of heart rate variability. In addition, an in vivo electrophysiological study entailed programmed electrical stimulation using an intracardiac octapolar catheter. Results: Panx1 deficiency results in a higher incidence of AV-block, delayed ventricular depolarisation, significant prolongation of QT- and rate corrected QT-interval and a higher incidence of atrial fibrillation after intraatrial burst stimulation. Conclusion: Panx1 seems to play an important role in murine cardiac electrophysiology and warrants further consideration in the context of hereditary forms of atrial fibrillation.

Streaming updates for heart rate variability algorithms.

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

Heart rate variablity indices are poor predictors of exercise capacity in healthy type 1 diabetics (LB664)

People with diabetes often have chronotropic incompetence and reduced exercise tolerance. Chronotropic incompetence has been associated with autonomic dysfunction, thus we determined the ability of resting heart rate variability (HRV) to predict an altered exercise response in healthy individuals with type 1 diabetes (T1D). Baseline measures were taken followed by quantification of HRV with power spectral analysis during controlled breathing (15 breaths·min‐1) in 20 T1D (10 male &amp; 10 female) screened for ischaemic cardiac disease and 12 age‐, weight‐, and training‐matched non‐diabetics (CON: 7 male &amp; 5 female). These measurements were followed by a VO2max test (with ECG). Resting HR (73 ± 10 vs. 64 ± 10 beats·min‐1; P = 0.01) was higher and mean arterial blood pressure (92 ± 12 vs. 90 ± 11 mm Hg; P = 0.68) was not different between T1D and CON respectively. HR reserve (104 ± 17 vs. 121 ± 11 beats·min‐1; P &lt; 0.001), VO2max (31 ± 9 vs. 41 ± 9 mL·min‐1·Kg‐1; P =0.005), HRV total power (1317 ± 1146 vs. 7493 ± 15080 ms2; P = 0.024), and HRV high frequency power (441 ± 491 vs. 3840 ± 9883 ms2; P = 0.029) were lower in T1D vs. CON respectively. Absolute max HR (177 ± 15 vs. 185 ± 10 beats·min‐1; P = 0.09), % of age predicted max HR (95 ± 6 vs. 97 ± 2 %; P = 0.07), and HRV low frequency power (328 ± 279 vs. 1672 ± 3433 ms2; P = 0.09) were not different in T1D vs. CON respectively. Despite group differences in HRV, linear correlations showed that resting HRV total, high frequency, and low frequency power accounted for &lt; 20% of the variability in resting HR, HR reserve, max HR, and VO2max in T1D. Resting HRV indices are poor predictors of cardiovascular exercise response in healthy T1D.Grant Funding Source: Supported by Anderson's trust

Heart Rate Variability and Complementary Medicine

Heart rate variability (HRV) is a parameter of the neurocontrol of the heart and is used more and more in recent scientific research and practice, not only in Western medicine, but also in evidence-based traditional medicine. Today innovative research including the latest recording technology and also artificial intelligence techniques are used for data acquisition and data analysis of HRV in acupuncture and herbal medicine research. The scope of HRV is not yet completely clear, but it is known that there are intraindividual and interindividual variances and that heart rate variation depends on age. It becomes less random with the aging process and the appearance of age-related diseases. Apart from age, circadian variations (sleep-wake cycle), physical condition, and mental and physical exertion are important influencing factors. HRV can also be affected by diverse conditions such as age-related diseases and so-called lifestyle diseases like diabetic neuropathy, renal failure, essential hypertension, cardiac disorders, coronary artery disease, or intracranial lesions. In all cases, different medications have to be taken into account. HRV can be used as a globally reliable indicator of the state of health. However, it could be demonstrated in this special issue that in special syndromes like stress or burnout one can counteract this process using different preventive methods like acupuncture. This special issue focuses on the latest innovative aspects concerning HRV and complementary medicine. Altogether, 25 manuscripts underwent a review process. Sixteen articles were accepted for publication. Some of the papers report results of HRV in combination with different kinds of acupuncture (manual needle acupuncture, laser acupuncture, and electroacupuncture). Important topics of this special issue are basic and clinical HRV research in complementary medicine, the development of innovative HRV-related concepts for assessing the state of health, new methods for the quantification of HRV, HRV and practical implications in traditional medicine, and telemedicine and HRV. This special issue contains basic research studies and clinical studies concerning menstrual pain and menstrual distress, burnout syndrome, depression, and insomnia, animal experimental studies in rats and dogs, and a review article.

The contribution of preintervention blood pressure, VO2max, BMI, autonomic function and gender to exercise-induced changes in heart rate variability

IntroductionThe quantification of heart rate variability (HRV) is a tool to assess the interaction between exercise and autonomic control, as well as the pathophysiology of diseases affecting autonomic function. Little...

A comparison between heart rate and heart rate variability as indicators of cardiac health and fitness

Quantification of cardiac autonomic activity and control via heart rate (HR) and heart rate variability (HRV) is known to provide prognostic information in clinical populations. Issues with regard to standardization and interpretation of HRV data make the use of the more easily accessible HR on its own as an indicator of autonomic cardiac control very appealing. The aim of this study was to investigate the strength of associations between an important cardio vascular health metric such as VO2max and the following: HR, HRV indicators, and HR normalized HRV indicators. A cross sectional descriptive study was done including 145 healthy volunteers aged between 18 and 22 years. HRV was quantified by time domain, frequency domain and Poincaré plot analysis. Indirect VO2max was determined using the Multistage Coopers test. The Pearson correlation coefficient was calculated to quantify the strength of the associations. Both simple linear and multiple stepwise regressions were performed to be able to discriminate between the role of the individual indicators as well as their combined association with VO2max. Only HR, RR interval, and pNN50 showed significant (p < 0.01, p < 0.01, and p = 0.03) correlations with VO2max. Stepwise multiple regression indicated that, when combining all HRV indicators the most important predictor of cardio vascular fitness as represented by VO2max, is HR. HR explains 17% of the variation, while the inclusion of HF (high frequency HRV indicator) added only an additional 3.1% to the coefficient of determination. Results also showed when testing the normalized indicators, HR explained of the largest percentage of the changes in VO2max (16.5%). Thus, HR on its own is the most important predictor of changes in an important cardiac health metric such as VO2max. These results may indicate that during investigation of exercise ability (VO2max) phenomena, quantification of HRV may not add significant value.

Abstract P315: Increase in Stress May Contribute to Lower Heart Rate Variability in Healthy Young Subjects

Background: Stress is one of the risk factors for cardiovascular disease and decreased heart rate variability is associated to increased mortality in some cardiac diseases. The aim of the study was to assess the impact of perceived stress on cardiac autonomic regulation in young healthy volunteers. Methods: 35 young healthy volunteers (19 to 29 years old, 6 men) from a Brazilian population were assessed for perceived stress by the translated and validated Perceived Stress Scale (PSS, 14 questions) and had the R-R intervals recorded at rest on supine position (POLAR RS800CX) and analyzed (5 minutes, Kubius HRV software) by Fast-Fourier Transform for quantification of Heart Rate Variability (HRV). Results: Average data (±SD) for age, heart rate, BMI, waist circumference and percentage of body fat (%BF) were: 21.3±2.7 years; 65.5±7.9 bpm; 22.3±1.9 Kg/m 2 ; 76.0±6.1 cm and 32.1±6.6%; respectively. The mean score for the PSS-14 was 23.5±7.2 and for the HRV parameter as follow: SSDN=54.8±21.2ms; rMSSD=55.9±32.2ms; low-frequency (LF)= 794.8±579.7ms 2 ; High-frequency (HF)= 1508.0±1783.0 ms 2 ; LF(n.u.)= 41.1±16.2; HF(n.u.)= 58.9±16.2; LF/HF=0.89±0.80 and Total power (TP)= 3151±2570ms 2 . Spearman nonparametric correlation was calculated and there was a significant correlation of PSS-14 scores and LF (ms 2 ) (r=−0.343; p= 0.044). Other HRV variables did not shown significant correlation but also had negative values for Spearman r (TP r=−0.265, p=0.124; HF r=−0.158; SSDN r=−0.207; rMSSD r=−0.243, p=0.160). LF/HF and LF(n.u.) did not correlate to PSS-14 having Spearman r very close to zero (LF/HF r=−0.007, p=0.969; LF(n.u.) r=−0.005, p=0.976). No correlation was found for HRV parameters and BMI and there was a trend for statistical correlation of %BF and LF (ms 2 ) (r=−0.309, p=0.071). Conclusions: These data demonstrate a possible association of perceived stress level and HRV at rest. Changes in LF can be a consequence of both sympathetic and parasympathetic activity, however, analyzing the other variables HF, TP, SSDN and rMSSD (all negative Spearman r) and due to the lack of changes in LF/HF ratio and LF(n.u.) we interpret that increased stress may be associated to decrease in overall heart rate variability. These changes were seen in healthy individuals and may point out an important mechanism in cardiovascular disease development.

Sample entropy-based HRV characterisation

The non-linear methods-based heart rate variability analyses are widely becoming popular as these are based upon analysing original RR-interval series. The sample entropy (SampEn) is one of these methods used for quantifying signal complexity, and is based upon hypothesis that decreasing entropy is a pointer towards loss of heart rate variability (HRV). This paper characterises the sample entropy-based HRV quantification in healthy subjects of different age groups. It clearly demonstrates that sample entropy decreases in higher age groups, which indicates loss of HRV. The tolerance or threshold for template matching affects the entropy magnitude of cardiovascular time series, but it remains nearly same at fixed value of ‘r’ for data lengths of the order of 10m to 30m.

Importance of Tachogram Length and Period of Recording during Noninvasive Investigation of the Autonomic Nervous System

Various disciplines use quantification of heart rate variability (HRV) as an indicator of autonomic function without recognizing the importance of using standardized methodologies. The aim of this study was to assess the influence of tachogram length and period of recording on HRV indicator values. To test the influence of the length of the recording time, HRV indicator values calculated from 180, 300, 420, and 600 seconds recording periods (supine and standing) were compared. Also individual 3- minute periods (0-180 seconds, 180-360 seconds, 360-540 seconds) from 10-minute (supine and standing) tachograms were compared. In the supine position, vagal-efferent HRV indicators were not influenced by tachogram length varying between 3 and 10 minutes (P > 0.05). Supine HRV indicator values representing combined sympathetic and parasympathetic influences were tachogram length-dependent (P < 0.05). During orthostatic stress all HRV indicators were tachogram length-dependent (P < 0.05). Upon standing up marked vagal withdrawal and sympathetic activation occurred. Vagal withdrawal tapered off during the 3-6-minute period after rising while indicators representing combined vagal and sympathetic activation were totally reversed. During application of orthostatic stress the exact starting point of recording, as well as the length of recording, is critical due to the activation and normalization of homeostatic mechanisms. Starting the tachogram recording too late will miss out on part of the initial response to change in body position. Longer recording times will give a combination of values recorded during the stress response and values obtained after stabilization in the standing position.

Quantification of Heart Rate Variability (HRV) Data using Symbolic Entropy to Distinguish between Healthy and Disease Subjects

Quantification of Heart Rate Variability (HRV) Data using Symbolic Entropy to Distinguish between Healthy and Disease Subjects

T‐Wave Variability Detects Abnormalities in Ventricular Repolarization: A Prospective Study Comparing Healthy Persons and Olympic Athletes

Sudden cardiac death in athletes is more common than in the general population. Routine screening procedures are performed to identify competitors at risk. A new Holter-based parameter analyzes variation of the ventricular repolarization (TVar). The aim of this study was to evaluate differences in electrocardiogram (ECG), Echo, and Holter (H) in competitive athletes compared to a healthy control group consisting of medical students (MS). A total of 40 athletes (19 females, Olympic team, Luxembourg) and 40 MS (22 females) were examined by means of a resting ECG, treadmill exercise (TE), echocardiogram (Echo), as well as H recordings during a routine screening visit. To analyze TVar, a 20-minute H recording at rest (sampling rate 1000 per second) was performed. Moreover, heart rate variability (HRV) as well as HR turbulence (HRT) was computed. No differences in demographic variables were detected. Quantification of HRV detected a significant increase in the vagal component of autonomic cardiac modulation. In contrast, no differences for HRT were found. Echo parameter demonstrated a thicker septal wall without differences of the posterior wall. TVar values were normal in range, but did differ significantly between the two groups. No correlation between TVar and echo as well as Holter parameters was detected. TVar was able to demonstrate significant differences in terms of alterations of ventricular activation. This might indicate an early change of myocardial repolarization representing a substrate for life-threatening arrhythmia. Larger studies on the predictive value of TVar including follow-up are necessary to confirm this preliminary finding.