scholarly journals Heart Rate Variability Analysis and The Influence of Exercise Intensity Over Time in Young-healthy Women

2021 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Aisha Widi Rahayu ◽  
Izza Alifa Hassya ◽  
Eki Dipo Laksono ◽  
Alvin Sahroni
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Exercise Intensity ◽  
Circulatory System ◽  
Heart Rate Variability Analysis ◽  
Regular Exercise ◽  
Poincaré Plot ◽  
Evaluation Period ◽  
Healthy Women ◽  
Exercise Period

Our heart is a vital organ that pumps blood and through the vessels of the circulatory system. In medical applications, we can observe the heart rate using Electrocardiograph (ECG). Currently, people tend to have high working activity without a proper exercise intensity. This study was conducted to observe the heart rate variability (HRV) on the healthy young woman who was not doing any exercise. We evaluated the HRV characteristics while exercising with a regular period and different intensity (light to hard) and how the difference before and after of evaluation period. Seven young-healthy women (19 - 21 years old) women were observed during three observation stages: pre-exercise, main exercise-period, and post-exercise for 2 months. We analyzed MeanRR, SDRR, CVRR, rMSSD, VLF, LF, HF, and the Poincaré plot parameters (SD1 and SD2) as the HRV properties. We found that SDRR was decreased from the first week (0.08 s) to the last week of the evaluation period (0.03 s) followed by the HF component (0.15 – 0.2 Hz). The Poincaré plot properties also reduced from the first week to the last week of the exercise period (0.07 s to 0.03 s). We indicated the characteristics of a woman's HRV during regular exercise periods with different intensity have made the heart more effective in pumping blood. We concluded that the heart condition would be improved during regular exercise with the increment of intensity even in a short of a period. Finally, the heart rate performance may be decreased during absent from regular exercise for a month.

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 Heart rate variability analysis based on modified Poincaré plot

2012 ◽  
Vol 61 (19) ◽  
pp. 190506
Author(s):  
Huo Cheng-Yu ◽  
Zhuang Jian-Jun ◽  
Huang Xiao-Lin ◽  
Hou Feng-Zhen ◽  
Ning Xin-Bao
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Heart Rate Variability Analysis ◽  
Poincaré Plot ◽  
Variability Analysis

scholarly journals Validity of the Polar H7 Heart Rate Sensor for Heart Rate Variability Analysis during Exercise in Different Age, Body Composition and Fitness Level Groups

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 902
Author(s):  
Adrián Hernández-Vicente ◽  
David Hernando ◽  
Jorge Marín-Puyalto ◽  
Germán Vicente-Rodríguez ◽  
Nuria Garatachea ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Body Composition ◽  
Excellent Agreement ◽  
Exercise Intensity ◽  
Heart Rate Variability Analysis ◽  
Fat Percentage ◽  
Fitness Level ◽  
Hrv Analysis ◽  
Frequency Power

This work aims to validate the Polar H7 heart rate (HR) sensor for heart rate variability (HRV) analysis at rest and during various exercise intensities in a cohort of male volunteers with different age, body composition and fitness level. Cluster analysis was carried out to evaluate how these phenotypic characteristics influenced HR and HRV measurements. For this purpose, sixty-seven volunteers performed a test consisting of the following consecutive segments: sitting rest, three submaximal exercise intensities in cycle-ergometer and sitting recovery. The agreement between HRV indices derived from Polar H7 and a simultaneous electrocardiogram (ECG) was assessed using concordance correlation coefficient (CCC). The percentage of subjects not reaching excellent agreement (CCC > 0.90) was higher for high-frequency power (PHF) than for low-frequency power (PLF) of HRV and increased with exercise intensity. A cluster of unfit and not young volunteers with high trunk fat percentage showed the highest error in HRV indices. This study indicates that Polar H7 and ECG were interchangeable at rest. During exercise, HR and PLF showed excellent agreement between devices. However, during the highest exercise intensity, CCC for PHF was lower than 0.90 in as many as 60% of the volunteers. During recovery, HR but not HRV measurements were accurate. As a conclusion, phenotypic differences between subjects can represent one of the causes for disagreement between HR sensors and ECG devices, which should be considered specifically when using Polar H7 and, generally, in the validation of any HR sensor for HRV analysis.

scholarly journals A novel approach in heart-rate-variability analysis

2021 ◽  
Author(s):  
Andras Buzas ◽  
Tamas Horvath ◽  
Andras Der
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Extended Period ◽  
Evaluation Procedure ◽  
Heart Rate Variability Analysis ◽  
Daily Routine ◽  
Poincaré Plot ◽  
Statistical Measures ◽  
Registration Period

Heart-rate variability (HRV), measured by the fluctuation of beat-to-beat intervals, has been growingly considered the most important hallmark of heart rate (HR) time series. HRV can be characterized by various statistical measures both in the time and frequency domains, or by nonlinear methods. During the past decades, an overwhelming amount of HRV data has been piled up in the research community, but the individual results are difficult to reconcile due to the different measuring conditions and the usually HR-dependent statistical HRV-parameters applied. Moreover, the precise HR-dependence of HRV parameters is not known. Using data gathered by a wearable sensor of combined heart-rate and actigraphy modalities, here, we introduce a novel descriptor of HRV, based on a modified Poincare plot of 24-h RR-recordings. We show that there exists a regressive biexponential HRV versus HR master curve (M-curve) that is highly conserved for a healthy individual on short and medium terms (on the hours to months scale, respectively). At the same time, we reveal how this curve is related to age in the case of healthy people, and establish alterations of the M-curves of heart-attack patients. A stochastic neuron model accounting for the observed phenomena is also elaborated, in order to facilitate physiological interpretation of HRV data. Our novel evaluation procedure applied on the time series of interbeat intervals allows the description of the HRV(HR) function with unprecedented precision. To utilize the full strength of the method, we suggest a 24-hour-long registration period under natural, daily-routine circumstances (i.e., no special measuring conditions are required). By establishing a patient's M-curve, it is possible to monitor the development of his/her status over an extended period of time. On these grounds, the new method is suggested to be used as a competent tool in future HRV analyses for both clinical and training applications, as well as for everyday health promotion.

Poincaré Plot Methods for Heart Rate Variability Analysis

2013 ◽  
Author(s):  
Ahsan Habib Khandoker ◽  
Chandan Karmakar ◽  
Michael Brennan ◽  
Marimuthu Palaniswami ◽  
Andreas Voss
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Heart Rate Variability Analysis ◽  
Poincaré Plot ◽  
Variability Analysis

scholarly journals On the Minimal Adequate Sampling Frequency of the Photoplethysmogram for Pulse Rate Monitoring and Heart Rate Variability Analysis in Mobile and Wearable Technology

2019 ◽  
Vol 19 (5) ◽  
pp. 232-240 ◽  
Author(s):  
Szabolcs Béres ◽  
Lőrinc Holczer ◽  
László Hejjel
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Pulse Rate ◽  
Sampling Error ◽  
Sampling Theorem ◽  
Sampling Frequency ◽  
Heart Rate Variability Analysis ◽  
Poincaré Plot ◽  
Adequate Sampling

Abstract Recently there has been great interest in photoplethysmogram signal processing. However, its minimally necessary sampling frequency for accurate heart rate variability parameters is ambiguous. In the present paper frequency-modulated 1.067 Hz cosine wave modelled the variable PPG in silico. The five-minute-long, 1 ms resolution master-signals were decimated (D) at 2-500 ms, then cubic spline interpolated (I) back to 1 ms resolution. The mean pulse rate, standard deviation, root mean square of successive pulse rate differences (RMSSD), and spectral components were computed by Varian 2.3 and compared to the master-series via relative accuracy error. Also Poincaré-plot morphology was assessed. Mean pulse rate is accurate down to 303 ms (D) and 400 ms (I). In low-variability series standard deviation required at least 5 ms (D) and 100 ms (I). RMSSD needed 10 ms (D), and 303 ms (I) in normal, whereas 2 ms (D) and 100 ms (I) in low- variability series. In the frequency domain 5 ms (D) and 100 ms (I) are required. 2 ms (D) and 100 ms (I) preserved the Poincaré-plot morphology. The minimal sampling frequency of PPG for accurate HRV analysis is higher than expected from the signal bandwidth and sampling theorem. Interpolation improves accuracy. The ratio of sampling error and expected variability should be considered besides the inherent sensitivity of the given parameter, the interpolation technique, and the pulse rate detection method.

scholarly journals Field Monitoring the Effects of Overnight Shift Work on Specialist Tactical Police Training with Heart Rate Variability Analysis

2021 ◽  
Vol 13 (14) ◽  
pp. 7895
Author(s):  
Colin Tomes ◽  
Ben Schram ◽  
Robin Orr
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Shift Work ◽  
Emergency Response ◽  
Police Training ◽  
Heart Rate Variability Analysis ◽  
Response Team ◽  
Hrv Analysis ◽  
And Performance ◽  
Personnel Allocation

Police work exposes officers to high levels of stress. Special emergency response team (SERT) service exposes personnel to additional demands. Specifically, the circadian cycles of SERT operators are subject to disruption, resulting in decreased capacity to compensate in response to changing demands. Adaptive regulation loss can be measured through heart rate variability (HRV) analysis. While HRV Trends with health and performance indicators, few studies have assessed the effect of overnight shift work on HRV in specialist police. Therefore, this study aimed to determine the effects overnight shift work on HRV in specialist police. HRV was analysed in 11 SERT officers and a significant (p = 0.037) difference was found in pRR50 levels across the training day (percentage of R-R intervals varying by >50 ms) between those who were off-duty and those who were on duty the night prior. HRV may be a valuable metric for quantifying load holistically and can be incorporated into health and fitness monitoring and personnel allocation decision making.

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