scholarly journals The Use of Percent Change in RR Interval for Data Exclusion in Analyzing 24-h Time Domain Heart Rate Variability in Rodents

2019 ◽  
Vol 10 ◽  
Author(s):  
Emma Karey ◽  
Shiyue Pan ◽  
Amber N. Morris ◽  
Donald A. Bruun ◽  
Pamela J. Lein ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Domain ◽  
Rr Interval ◽  
Percent Change

scholarly journals Heart rate and heart rate variability comparison between postural orthostatic tachycardia syndrome versus healthy participants; a systematic review and meta-analysis.

2019 ◽  
Author(s):  
Joel Dominic Swai ◽  
Zixuan Hu ◽  
Xiexiong Zhao ◽  
Tibera Rugambwa ◽  
Gui Ming
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain ◽  
Time Domain ◽  
Meta Analysis ◽  
Postural Orthostatic Tachycardia Syndrome ◽  
Mean Values ◽  
Rr Interval ◽  
Normalized Units ◽  
Healthy Participants

Abstract Background: A number of published literatures have reported that, physiologically, heart rate variability in patients with postural orthostatic tachycardia (POTS) to be greatly confounded by age, sex, race, physical fitness and circadian rhythm. The purpose of this study was to compare between postural orthostatic tachycardia syndrome (POTS) patients versus healthy patients , in terms of heart rate (HR) and heart rate variability (HRV) after Head-Up tilt test, by systematic review and meta-analysis of available published literature. Methods: MEDLINE (using PubMed interphase), EMBASE and SCOPUS were systematically searched for observational studies comparing between POTS patients versus healthy patients, in terms of heart rate (HR) and heart rate variability (HRV). HRV was grouped into Time and frequency domain outcome measurements. Time domain was measured as mean RR- interval and mean square root of mean of squares of successive R-R waves (rMSSD) in milliseconds. Frequency domain was measured as mean values of Low frequency power (LF), High frequency power (HF), LF/HF-ratio, LF-normalized units (LF(n.u)) and HF-normalized units (HF(n.u)). Demographic data, comorbidities, and mean values of HR, RR- interval, rMSSD, LF, HF, LF/HF-ratio, LF-(n.u) and H.F-n.u were extracted from each group and compared, by their mean differences as overall outcome measure. Computer software, RevMan 5.3 was utilized, at 95% significance level. Results: Twenty (20) eligible studies were found to report 717 POTS and 641 healthy participant s. POTS group had higher mean heart rate (p<0.05), lower mean RR-Interval (p<0.05), lower rMSSD (p<0.05) than healthy participants. Furthermore, POTS group had lower mean HF (p>0.05), lower mean LF(p>0.05), and lower mean HF(n.u) (p>0.05), higher LF/HF-Ratio (p>0.05) and higher LF(n.u) (p>0.05) as compared to healthy participants. Conclusion: POTS patients have higher heart rate than healthy patients after HUTT and and lower HRV in terms of time domain measure but not in terms of frequency domain measure. Heart rate and time domain analyses of HRV are more reliable than frequency domain analysis in differentiating POTS patient from healthy participant. We call upon sensitivity and specificity studies.

scholarly journals Fuzzy logic approach for heart rate variability

2021 ◽  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Fuzzy Logic ◽  
Frequency Domain ◽  
Time Domain ◽  
Normal Sinus Rhythm ◽  
Free Access ◽  
Massachusetts Institute Of Technology ◽  
Scientific Publications ◽  
Rr Interval

Intrioution. The heart rate variability (HRV) is based on measuring (time) intervals between R-peaks (of RR-intervals) of an electrocardiogram (ECG) and plotting a rhythmogram on their basis with its subsequent analysis by various mathematical methods that are classified as Time Domain (TD), Frequency Domain (FD) and Nonlinear (NM) [1, 2]. Diversity of methods and approaches to analysis of HRV is stemming from complexity and nonlinearity of the phenomenon itself, as well as from greater diversity of physiological reactions of an organism, both in normal and pathological states. Therefore, it appears relevant and important to incorporate currently existing HRV indicators and norms into a unified Fuzzy Logic (FL) methodology, which in turn will allow to integrally assess each metric and HRV results as a whole. Objective. We propose a Fuzzy Logic algorithm for incorporating into a single view of each metric, – Time Domain, Frequency Domain, Nonlinear Methods and HRV as a whole. Materials and methods. We define by FL the extent of belonging to normal state both for each distinct HRV metric – TD, FD and NM, and for a patient's HRV in general. Membership functions of any HRV index and defuzzification rules for FL scores was defined. In order to implement the proposed algorithm, specified parameters of mean values of HRV (М) indicators and their standard deviation (σ) have been found in scientific publications on HRV [1, 3, 7, 8, 9, 10]. We use for FL algorithm demonstration a long-term HRV records by Massachusetts Institute of Technology - Boston’s Beth Israel Hospital (MIT-BIH) from [11], a free-access, on-line archive of physiological signals for Normal Sinus Rhythm (NSR) RR Interval, Congestive Heart Failure (CHF) RR Interval and Atrial Fibrillation (AF) Databases [12]. Conclusion. In this article, we have presented a comprehensive view of HRV by Fuzzy Logic technology and thoroughly examined the peculiarities of its application and interpretation. Of all considered examples of FL analysis, the worst result is demonstrated by a patient from the AF group, while the best one belongs to a patient from the NSR group. Difference in FL Scores between these patients from NSR and CHF groups is almost 4 times, while between patients from NSR and АF groups it is almost 6 times. It appears especially important to implement such a design in portable medical devices for quick and easy interpretation of numerous parameters measured by them.

scholarly journals Heart rate variability: Analysis of time-domain indices in patients with chronic Chagas disease before and after an exercise program

2013 ◽  
Vol 32 (3) ◽  
pp. 219-227 ◽  
Author(s):  
Marcus Vinicius Amaral da Silva Souza ◽  
Carla Cristiane Santos Soares ◽  
Juliana Rega de Oliveira ◽  
Cláudia Rosa de Oliveira ◽  
Paloma Hargreaves Fialho ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Chagas Disease ◽  
Time Domain ◽  
Exercise Program ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis ◽  
Before And After ◽  
Chronic Chagas Disease

Effect of short-term mental stress on time- domain indices of heart rate variability in obese individuals – A case- control study

Biomedicine ◽  
2021 ◽  
Vol 41 (2) ◽  
pp. 274-277
Author(s):  
Priya S.A. ◽  
R. Rajalakshmi
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Neuronal Activity ◽  
Time Domain ◽  
Mental Stress ◽  
Mental Arithmetic ◽  
High Body Mass Index ◽  
Control Group ◽  
Stress Task ◽  
The Individual

  Introduction and Aim: Mental stress may impact dramatically on dynamic autonomic control on heart. Many studies have demonstrated association of high body mass index (BMI) with greater risk for cardiovascular disease with disturbance in autonomic neuronal activity. Analysis of Heart rate variability (HRV)during acute mental stress assesses the autonomic status of the individual. Hence, we aimed to study the effect of acute mental stress on time domain measures in obese adults.   Materials and Methods:Sixty male volunteers of 30 each in study group (obese individuals) and control group (non-obese individuals) were recruited for the study. A basal recording of ECG in lead II was done on all the individuals. Then they underwent mental arithmetic stress task for 5 minutes during which again ECG was recorded. The change in time domain measures of HRV during rest and stress task was analyzed and compared between both the groups.   Results: Analysis of time domain measures of HRV revealed a statistically significant increase (p ? 0.001) in mean heart rate in both obese and non-obese individuals, while rMSSD(root mean square differences of successive RR interval) and SDNN (standard deviation of all NN intervals) showed a statistically significant (p? 0.001) decrease in obese individuals and non-obese individuals did not show any statistically significant change during the mental stress task.   Conclusion: In response to acute mental stress there was increased heart rate in both the groups. But the autonomic neuronal activity differed by way of sympathetic dominance in non-obese individuals and parasympathetic withdrawal in obese individuals.  

Trends in Heart Rate and Heart Rate Variability during Pregnancy and Three Months Postpartum: Continuous Monitoring in a Free-Living Context (Preprint)

2021 ◽  
Author(s):  
Fatemeh Sarhaddi ◽  
Iman Azimi ◽  
Anna Axelin ◽  
Hannakaisa Niela-Vilen ◽  
Pasi Liljeberg ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pregnant Women ◽  
Time Domain ◽  
Second Trimester ◽  
Third Trimester ◽  
Non Invasive ◽  
The Third ◽  
Invasive Method ◽  
Frequency Power

BACKGROUND Heart rate variability (HRV) is a non-invasive method reflecting autonomic nervous system (ANS) regulations. Altered HRV is associated with adverse mental or physical health complications. ANS also has a central role in physiological adaption during pregnancy causing normal changes in HRV. OBJECTIVE Assessing trends in heart rate (HR) and HRV parameters as a non-invasive method for remote maternal health monitoring during pregnancy and three months postpartum. METHODS Fifty-eight pregnant women were monitored using an Internet-of-Things (IoT)-based remote monitoring system during pregnancy and 3-months postpartum. Pregnant women were asked to continuously wear Gear sport smartwatch to monitor their HR and HRV. In addition, a cross-platform mobile application was utilized for collecting pregnancy-related information. The trends of HR and HRV parameters were extracted using reliable data. We also analyzed the trends of normalized HRV parameters based on HR to remove the effect of HR changes on HRV trends. Finally, we exploited hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters. RESULTS HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P<.01). Time-domain HRV parameters, average normal interbeat intervals (AVNN), standard deviation of normal interbeat intervals (SDNN), root mean square of the successive difference of normal interbeat intervals (RMSSD), normalized SDNN (nSDNN), and normalized RMSSD (nRMSSD) decreased significantly during the second trimester (P<.001) then increased significantly during the third trimester (P<.01). Some of the frequency domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF (nHF) decreased significantly during the second trimester (P<.01), and HF increased significantly during the third trimester (P<.01). In the postpartum period, nRMSSD decreased (P<.05), and the LF to HF ratio (LF/HF) increased significantly (P<.01). CONCLUSIONS Our study showed that HR increased and HRV parameters decreased as the pregnancy proceeded, and the values returned to normal after the delivery. Moreover, our results show that HR started to decrease while time-domain HRV parameters and HF started to increase during the third trimester. Our results also demonstrate the possibility of continuous HRV monitoring in everyday life settings.

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