scholarly journals Validation of the Polar RS800CX for assessing heart rate variability during rest, moderate cycling and post-exercise recovery

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1501 ◽  
Author(s):  
Kyriakos I. Tsitoglou ◽  
Yiannis Koutedakis ◽  
Petros C. Dinas
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Recurrence Plot ◽  
Maximal Heart Rate ◽  
Poincaré Plot ◽  
Post Exercise ◽  
Plot Analysis ◽  
Mean Differences ◽  
Post Exercise Recovery ◽  
Electrocardiogram Ecg

Background: Heart rate variability (HRV) is an autonomic nervous system marker that provides reliable information for both disease prevention and diagnosis; it is also used in sport settings. We examined the validity of the Polar RS800CX heart rate monitor during rest, moderate cycling, and recovery in considering the total of 24 HRV indices. Method: A total of 32 healthy males (age=24.78±6.87 years, body mass index=24.48±3.13 kg/m2) completed a session comprised by three 20-minute time periods of resting, cycling at 60% of maximal heart rate, and recovery using a Polar RS800CX and an electrocardiogram (ECG) monitors. The HRV indices included time-domain, frequency-domain, Poincaré plot and recurrence plot. Bland–Altman plot analysis was used to estimate agreement between Polar RS800CX and ECG. Results: We detected significant associations (r>0.75, p<0.05) in all HRV indices, while five out of 24 HRV indices displayed significant mean differences (p<0.05) between Polar RS800CX and ECG during the resting period. However, for the exercise and recovery periods, we found significant mean differences (p<0.05) in 16/24 and 22/24 HRV indices between the two monitors, respectively. Conclusion: It is concluded that Polar RS800CX is a valid tool for monitoring HRV in individuals at resting conditions, but it displays inconsistency when used during exercise at 60% of maximal heart rate and recovery periods.

Characterization of HRV by Poincare Plot Analysis among the Female Tea Garden Workers of Northern Hilly Regions of West Bengal

2012 ◽  
pp. 206-215
Author(s):  
Somsirsa Chatterjee ◽  
Ankur Ganguly ◽  
Saugat Bhattacharya
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
West Bengal ◽  
Mean Value ◽  
Short Term ◽  
Poincaré Plot ◽  
Plot Analysis ◽  
Tea Garden

Recent research on Heart Rate Variability (HRV) has proven that Poincare Plot is a powerful tool to mark Short Term and Long Term Heart Rate Variability. This study focuses a comprehensive characterization of HRV among the Tea Garden Workers of the Northern Hilly Regions of West Bengal. The characterization, as available from the data sets, projects the average values of SD1 characteristics, that is, Short Term HRV in females as 58.265ms and SD2 as 149.474. The SDRR shows a mean value of 87.298 with a standard deviation of 119.669 and the S Characterization as 16505.99 ms and Standard deviation of 45882.31 ms. The SDRR shows a mean value of 87.298 with a standard deviation of 119.669 and the S Characterization as 16505.99 ms and Standard deviation of 45882.31 ms. ApEn Characterization showed mean value of 0.961 and standard deviation of 0.274.

Segmented Poincaré Plot Analysis for Risk Stratification in Patients with Dilated Cardiomyopathy

2010 ◽  
Vol 49 (05) ◽  
pp. 511-515 ◽  
Author(s):  
C. Fischer ◽  
R. Schroeder ◽  
H. R. Figulla ◽  
M. Goernig ◽  
A. Voss
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Risk Stratification ◽  
Dilated Cardiomyopathy ◽  
Heart Rate Variability Analysis ◽  
Poincaré Plot ◽  
Plot Analysis ◽  
Interval Time Series ◽  
Nonlinear Features ◽  
Cloud Of Points

Summary Background: The prognostic value of heart rate variability in patients with dilated cardiomyopathy (DCM) is limited and does not contribute to risk stratification although the dynamics of ventricular repolarization differs considerably between DCM patients and healthy subjects. Neither linear nor nonlinear methods of heart rate variability analysis could discriminate between patients at high and low risk for sudden cardiac death. Objective: The aim of this study was to analyze the suitability of the new developed segmented Poincaré plot analysis (SPPA) to enhance risk stratification in DCM. Methods: In contrast to the usual applied Poincaré plot analysis the SPPA retains nonlinear features from investigated beat-to-beat interval time series. Main features of SPPA are the rotation of cloud of points and their succeeded variability depended segmentation. Results: Significant row and column probabilities were calculated from the segments and led to discrimination (up to p < 0.005) between low and high risk in DCM patients. Conclusion: For the first time an index from Poincaré plot analysis of heart rate variability was able to contribute to risk stratification in patients suffering from DCM.

scholarly journals Is Ultra-Short-Term Heart Rate Variability Valid in Non-static Conditions?

2021 ◽  
Vol 12 ◽  
Author(s):  
Jin Woong Kim ◽  
Hyeon Seok Seok ◽  
Hangsik Shin
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Measurement Time ◽  
Exercise Recovery ◽  
Short Term ◽  
Post Exercise ◽  
Hrv Analysis ◽  
Time Required ◽  
Static Conditions ◽  
Post Exercise Recovery

In mobile healthcare, heart rate variability (HRV) is increasingly being used in dynamic patient states. In this situation, shortening of the measurement time is required. This study aimed to validate ultra-short-term HRV in non-static conditions. We conducted electrocardiogram (ECG) measurements at rest, during exercise, and in the post-exercise recovery period in 30 subjects and analyzed ultra-short-term HRV in time and frequency domains by ECG in 10, 30, 60, 120, 180, and 240-s intervals, and compared the values to the 5-min HRV. For statistical analysis, null hypothesis testing, Cohen’s d statistics, Pearson’s correlation coefficient, and Bland-Altman analysis were used, with a statistical significance level of P &lt; 0.05. The feasibility of ultra-short-term HRV and the minimum time required for analysis showed differences in each condition and for each analysis method. If the strict criteria satisfying all the statistical methods were followed, the ultra-short-term HRV could be derived from a from 30 to 240-s length of ECG. However, at least 120 s was required in the post-exercise recovery or exercise conditions, and even ultra-short-term HRV was not measurable in some variables. In contrast, according to the lenient criteria needed to satisfy only one of the statistical criteria, the minimum time required for ultra-short-term HRV analysis was 10–60 s in the resting condition, 10–180 s in the exercise condition, and 10–120 s in the post-exercise recovery condition. In conclusion, the results of this study showed that a longer measurement time was required for ultra-short-term HRV analysis in dynamic conditions. This suggests that the existing ultra-short-term HRV research results derived from the static condition cannot applied to the non-static conditions of daily life and that a criterion specific to the non-static conditions are necessary.

Decrease in heart rate variability with overtraining: assessment by the Poincare plot analysis

2004 ◽  
Vol 24 (1) ◽  
pp. 10-18 ◽  
Author(s):  
Laurent Mourot ◽  
Malika Bouhaddi ◽  
Stephane Perrey ◽  
Sylvie Cappelle ◽  
Marie-Therese Henriet ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Poincaré Plot ◽  
Plot Analysis

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

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 309 ◽  
Author(s):  
Teresa Henriques ◽  
Maria Ribeiro ◽  
Andreia Teixeira ◽  
Luísa Castro ◽  
Luís Antunes ◽  
...  
Keyword(s):  
Heart Rate ◽  
Recurrence Plot ◽  
Multiscale Entropy ◽  
Fluctuation Analysis ◽  
Mathematical Methods ◽  
Poincaré Plot ◽  
Nonlinear Methods ◽  
Plot Analysis ◽  
Heart Rate Dynamics ◽  
Recurrence Plot Analysis

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.

scholarly journals Decreased heart rate variability in patients with cirrhosis relates to the presence and degree of hepatic encephalopathy

2009 ◽  
Vol 296 (2) ◽  
pp. G330-G338 ◽  
Author(s):  
Ali R. Mani ◽  
Sara Montagnese ◽  
Clive D. Jackson ◽  
Christopher W. Jenkins ◽  
Ian M. Head ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Hepatic Encephalopathy ◽  
Inflammatory Cytokines ◽  
Clinical Settings ◽  
Poincaré Plot ◽  
Risk Of Death ◽  
Plot Analysis ◽  
Overt Hepatic Encephalopathy ◽  
Relative Risk Of Death

Heart rate variability (HRV) is reduced in several clinical settings associated with either systemic inflammation or neuropsychiatric impairment. The possibility that the changes in HRV observed in patients with neuropsychiatric impairment might relate to the overproduction of inflammatory cytokines does not seem to have been considered in the studies undertaken to date. HRV is decreased in patients with liver cirrhosis but its relationship to the impairment of neuropsychiatric performance, commonly observed in these patients, is unknown. The aim of this study was to investigate the relationship between HRV, hepatic encephalopathy, and production of inflammatory cytokines in patients with cirrhosis. Eighty patients with cirrhosis [53 men, 27 women; mean (±1SD) age 54 ± 10 yr], classified as neuropsychiatrically unimpaired or as having minimal or overt hepatic encephalopathy, and 11 healthy subjects were studied. HRV was assessed by applying Poincaré plot analysis to the R-R interval series on a 5-min ECG. Inflammatory cytokines (TNF-α, IL-6, IL-10, and IL-12) were measured in a subgroup of patients. Long-term R-R variability was significantly decreased in the patients with cirrhosis, in parallel with the degree of neuropsychiatric impairment ( P < 0.01) and independently of the degree of hepatic dysfunction ( P = 0.011). The relative risk of death increased by 7.7% for every 1-ms drop in this variable. Plasma levels of IL-6 significantly correlated with indexes of both HRV and neuropsychiatric performance. The changes observed in HRV and in neuropsychiatric status in patients with cirrhosis are significantly correlated, most likely reflecting a common pathogenic mechanism mediated by inflammatory cytokines.

NONLINEAR INDICES OF HEART RATE VARIABILITY FOR DIFFERENTIATING ARRHYTHMIAS

2013 ◽  
Vol 13 (04) ◽  
pp. 1350061 ◽  
Author(s):  
N. D. ASHA ◽  
K. PAUL JOSEPH
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Normal Sinus Rhythm ◽  
Ventricular Tachyarrhythmia ◽  
Recurrence Plot ◽  
Largest Lyapunov Exponent ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Poincaré Plot ◽  
Diagonal Length

Heart rate variability (HRV) is the temporal variation between sequences of consecutive heartbeats. Chaos and fractal-based measurements have been widely used for quantifying the HRV for cardiac risk stratification purposes. In this paper, five different sets of HRVs, viz., normal sinus rhythm (NSR), congestive heart failure (CHF), cardiac arrhythmia suppression trial (CAST), supra ventricular tachyarrhythmia (SVTA) and atrial fibrillation (AF), have been analysed using nonlinear parameters to fix the ranges of each parameter. Data were downloaded from the PhysioNet database with 15 sets in each case. The parameters used for analysis were Poincare plot measures: SD1, SD2 and SD12, largest Lyapunov exponent (LLE), correlation dimension (CD); recurrence plot measures: recurrence rate (REC), determinism (DET), mean diagonal length (L mean ), maximal diagonal length (L max ) and entropy (ENTR); detrended fluctuation analysis measures: scaling exponent (α) and fractal dimension (FD); sample entropy (SampEn); and approximate entropy (ApEn). Analysis of variance (ANOVA) was done for confirming the differences in parameter values between various cases. All parameters except LLE showed a significant statistical difference for different cases.

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