scholarly journals Universal inverse square relationship between heart rate variability and heart rate

2021 ◽  
Author(s):  
Anna V. Maltsev ◽  
Oliver Monfredi ◽  
Victor A. Maltsev
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Numerical Simulations ◽  
Explicit Formula ◽  
Exponential Decay ◽  
Taylor Series ◽  
Similar Relationship ◽  
Isolated Hearts ◽  
Diverse Species

AbstractIn our previous study, we analyzed heart rate variability and heart rate from a large variety of cardiac preparations (including humans, living animals, Langendorff-perfused isolated hearts, and single sinoatrial nodal cells) in diverse species, combining our data with those of previously published articles. The analysis revealed that regardless of conditions, heart rate variability (for the purposes of the study assessed as standard deviation) of beat-to-beat intervals heart rate follows a universal exponential decay-like relationship. Numerical simulations of DI variability by adding a randomly fluctuating term to net current revealed a similar relationship. In the present study, using a Taylor series, we found that this relationship is, in fact, inverse square, and we derive an explicit formula for sd(CL) vs. heart rate with biophysically meaningful parameters.

Autonomie Correlates of Antidepressant Treatment Using Heart-Rate Variability Analysis

1998 ◽  
Vol 43 (2) ◽  
pp. 183-186 ◽  
Author(s):  
Yaariv Khaykin ◽  
Paul Dorian ◽  
Brian Baker ◽  
Colin Shapiro ◽  
Paul Sandor ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Rating Scale ◽  
Antidepressant Treatment ◽  
Response To Therapy ◽  
Heart Rate Variability Analysis ◽  
Randomized Evaluation ◽  
Temporal Domain ◽  
Percentage Difference

Objective: To assess the 24-hour temporal-domain heart-rate variability correlates of treatment with fluoxetine or doxepinfor depression. Method: A randomized evaluation of fluoxetine and doxepin measured a 50% change in the Hamilton Depression Rating Scale (HDRS) score as a response to therapy and was correlated with measures of standard deviation of the mean of all 5-minute segments of normal electrocardiographic R-R intervals (SDANN), standard deviation of all normal R-R intervals (SDNN), root mean square of successive differences in R-R intervals (r-MSSD), and percentage difference between adjacent normal R-R intervals that are greater than 50 msec (pNN50)from 24-hour electrocardiogram (ECG) tapes. Results: Ten out of 14 patients responded. Response was associated with an increase in SDANN of 17% (P < 0.05). Nonresponse was associated with a 17% decrease in SDANN and a 22% decrease in SDNN (both P < 0.05). No other measures correlated with therapeutic response. No heart-rate variability (HRV) differences between the 2 drug therapies were observed. Conclusion: Twenty-four-hour HRV measures may be useful in assessing response to antidepressant therapy.

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.

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

2010 ◽  
Vol 5 (2) ◽  
pp. 49-59 ◽  
Author(s):  
Somsirsa Chatterjee ◽  
Ankur Ganguly ◽  
Saugat Bhattacharya
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
West Bengal ◽  
Mean Value ◽  
Data Sets ◽  
Short Term ◽  
Poincaré Plot ◽  
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.

scholarly journals P936 Early signs of cardiac dysfunction in obesity patients, results of the CARDIOBESE study

2020 ◽  
Vol 21 (Supplement_1) ◽  
Author(s):  
S M Snelder ◽  
L E De Groot - De Laat ◽  
L A Biter ◽  
M Castro Cabezas ◽  
N Pouw ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Cardiovascular Risk ◽  
Standard Deviation ◽  
Cardiovascular Risk Factors ◽  
Cardiac Dysfunction ◽  
P Value ◽  
Early Signs

Abstract Introduction Obesity is becoming a global epidemic. Current knowledge on early signs of cardiac dysfunction in obesity patients is insufficient. The onset of heart failure in obesity patients cannot be fully explained by the presence of traditional cardiovascular risk factors. Purpose To detect early signs of cardiac dysfunction in obesity patients without known cardiovascular disease. Methods The CARDIOBESE-study is a cross-sectional multicentre study of 100 obesity patients scheduled for bariatric surgery (body mass index (BMI) ≥35 kg/m2) without known cardiovascular disease, and 50 age-matched and gender-matched non-obese controls (BMI ≤30 kg/m2). Speckle tracking echocardiography, biomarkers and Holter monitoring were used to identify parameters that are able to show cardiac dysfunction at a very early stage in obesity patients. Results Obesity patients had impaired left ventricular ejection fraction, global longitudinal strain (GLS) and diastolic function parameters (e.g. septal e" velocity, lateral e" velocity, E/e’ and E/A-ratio) as compared to the non-obese controls (Table). C-reactive protein (CRP) and heart rate were increased, whereas heart rate variability (Standard deviation of NN intervals, SDNN) was decreased. Obesity patients were subdivided in patients with impaired (&lt; -17%, n = 56) or normal GLS (n = 36). Comparison between these patients revealed no differences regarding BMI, prevalence of traditional cardiovascular risk factors or CRP value. Nevertheless, patients with abnormal GLS had a higher waist circumference and lower SDNN. Conclusion There is a high prevalence of subclinical cardiac dysfunction as measured by GLS in obesity patients (56%), which appears to be related to abdominal fat and decreased heart rate variability and not to BMI, traditional cardiovascular risk factors or CRP. Non-obese controls (n = 50) Obesity patients (n = 100) p-value Obesity patients with normal GLS (n = 36) Obesity patients with impaired GLS (n = 56) p-value Age (years) 49.2 ± 9.5 47.9 ± 7.6 0.36 47.6 ± 7.1 48.3 ± 7.6 0.68 BMI (kg/m2) 24.9 ± 3.2 42.9 ± 4.1 &lt;0.001 42.7 ± 4.2 42.7 ± 4.1 0.98 Waist circumference (cm) 81.1 ± 10.4 133.1 ± 12.3 &lt;0.001 128.2 ± 11.5 135.2 ± 10.5 0.006 E/A- ratio 1.19 ± 0.26 1.01 ± 0.3 &lt;0.001 1.08 ± 0.2 0.96 ± 0.27 0.048 Septel e" velocity 10.3 ± 9.8 8.1 ± 1.8 0.03 8.2 ± 1.9 7.8 ± 1.7 0.24 E/e" 8.5 ± 2.1 8.9 ± 2.5 0.32 9.5 ± 2.4 8.7 ± 2.5 0.14 CRP (mg/L) 1.9 ± 2.9 8.8 ± 8.8 &lt;0.001 8.5 ± 7.3 9.3 ± 10.1 0.67 SDNN 160.2 ± 35.4 109.4 ± 46.0 &lt;0.001 130.4 ± 48.3 98.9 ± 41.2 0.001 Table: Selection of parameters. Values are means ± SD. SDNN= Standard deviation of NN intervals (heart rate variability)

Heart Rate Variability in Diabetes Patients

2006 ◽  
Vol 34 (3) ◽  
pp. 291-296 ◽  
Author(s):  
H Kudat ◽  
V Akkaya ◽  
AB Sozen ◽  
S Salman ◽  
S Demirel ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Frequency Domain ◽  
Autonomic Neuropathy ◽  
Time Domain ◽  
Cardiovascular Autonomic Neuropathy ◽  
Chronic Complications ◽  
Frequency Power ◽  
Diabetes Patients

Diabetes mellitus can cause cardiovascular autonomic neuropathy and is associated with increased cardiovascular deaths. We investigated cardiovascular autonomic neuropathy in diabetics and healthy controls by analysis of heart rate variability. Thirty-one diabetics and 30 age- and sex-matched controls were included. In the time domain we measured the mean R-R interval (NN), the standard deviation of the R-R interval index (SDNN), the standard deviation of the 5-min R - R interval mean (SDANN), the root mean square of successive R - R interval differences (RMSSD) and the percentage of beats with a consecutive R - R interval difference > 50 ms (pNN50). In the frequency domain we measured high-frequency power (HF), low-frequency power (LF) and the LF/HF ratio. Diabetes patients had lower values for time-domain and frequency-domain parameters than controls. Most heart rate variability parameters were lower in diabetes patients with chronic complications than in those without chronic complications.

scholarly journals Association of cardiac autonomic modulation with physical and clinical features of young people with type 1 diabetes

2016 ◽  
Vol 27 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Anne K. F. Silva ◽  
Diego G. D. Christofaro ◽  
Franciele M. Vanderlei ◽  
Marianne P. C. R. Barbosa ◽  
David M. Garner ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Type 1 Diabetes ◽  
Standard Deviation ◽  
Young Patients ◽  
Frequency Component ◽  
Parasympathetic Activity ◽  
High Frequency Component ◽  
Rr Intervals

ObjectiveThe objective of this study was to verify possible associations between heart rate variability indices and physical activity, body composition, and metabolic and cardiovascular parameters in individuals with type 1 diabetes.MethodA total of 39 young patients with type 1 diabetes were included. Body composition, physical activity, cardiovascular parameters, and metabolic parameters were assessed. For the heart rate variability analysis, heart rate was recorded beat-by-beat using a Polar S810i heart rate monitor for 30 minutes, with the volunteers in the supine position; subsequently, the following indices were considered: standard deviation of all normal RR intervals; root-mean square of differences between adjacent normal RR intervals in a time interval; percentage of adjacent RR intervals with a difference of duration >50 ms; high frequency component in milliseconds squared; high frequency component in normalised units; standard deviation of the instantaneous variability beat-to-beat; and standard deviation of the long-term variability. The association between the heart rate variability indices and independent variables was verified through linear regression in unadjusted and adjusted models (considering gender and age). The statistical significance was set at 5% and the confidence interval at 95%.ResultsHigh values of at-rest heart rate were associated with reduced parasympathetic activity and global heart rate variability, and higher values of waist-to-hip ratio were related to lower parasympathetic activity, independent of age or gender.ConclusionFor young patients with type 1 diabetes, increases in at-rest heart rate values are associated with reduced parasympathetic activity and global heart rate variability, whereas higher waist-to-hip ratio values are related to lower parasympathetic activity, both independent of age and gender.

scholarly journals Periodized Aerobic Interval Training Modifies Geometric Indices of Heart Rate Variability in Metabolic Syndrome

Medicina ◽  
2019 ◽  
Vol 55 (9) ◽  
pp. 532
Author(s):  
Laís Manata Vanzella ◽  
Denise Brugnoli Balbi Dagostinho ◽  
Maria Paula Ferreira de Figueiredo ◽  
Carlos Iván Mesa Castrillón ◽  
Jayme Netto Junior ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Interval Training ◽  
Training Group ◽  
Control Group ◽  
Autonomic Modulation ◽  
Poincaré Plot ◽  
Aerobic Interval Training

Background: Metabolic syndrome (MetS) influences the autonomic modulation, increasing the risk of cardiovascular events, which demands the identification of effective treatments for this population. Considering this, the study has the objective of evaluating the effects of periodized aerobic interval training (AIT) on geometrical methods of heart rate variability (HRV) on individuals with MetS. Methods: 52 individuals with MetS were considered for analysis. They were divided into two groups: aerobic interval training group (AITG; n = 26) and control group (CG; n = 26). The AITG performed 16 weeks of periodized AIT. For HRV analysis, the heart rate was recorded beat-by-beat at the beginning and the end of the AIT program and geometrical methods were used for analysis. Results: significant increase was observed for triangular index (RRtri, −1.25 ± 0.58 vs. 1.41 ± 0.57), standard deviation of distances from diagonal to points (SD1, −0.13 ± 1.52 vs. 4.34 ± 1.49), and standard deviation of distances from points to lines (SD2, −2.14 ± 3.59 vs. 11.23 ± 3.52) on AITG compared to CG. Significant differences were not observed for triangular interpolation of normal heartbeats interval histogram (TINN, −4.05 ± 17.38 vs. 25.52 ± 17.03) and SD1/SD2 ratio (0.03 ± 0.02 vs. 0.00 ± 0.02). Qualitative analysis of the Poincaré plot identified increase on dispersion of both short and long-term intervals between successive heartbeats (RR interval) on AITG after the AIT program. Conclusion: geometric indices of HRV suggest an increase in cardiac autonomic modulation in individuals with MetS after 16 weeks of periodized AIT.

Associations between heart rate variability, metabolic syndrome risk factors, and insulin resistance

2015 ◽  
Vol 40 (7) ◽  
pp. 734-740 ◽  
Author(s):  
Melanie I. Stuckey ◽  
Antti Kiviniemi ◽  
Dawn P. Gill ◽  
J. Kevin Shoemaker ◽  
Robert J. Petrella
Keyword(s):  
Risk Factors ◽  
Insulin Resistance ◽  
Metabolic Syndrome ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Waist Circumference ◽  
Frequency Domain ◽  
Model Assessment ◽  
Poincaré Plot

The purpose of this study was to examine differences in heart rate variability (HRV) in metabolic syndrome (MetS) and to determine associations between HRV parameters, MetS risk factors, and insulin resistance (homeostasis model assessment for insulin resistance (HOMA-IR)). Participants (n = 220; aged 23–70 years) were assessed for MetS risk factors (waist circumference, blood pressure, fasting plasma glucose, triglycerides, and high-density lipoprotein cholesterol) and 5-min supine HRV (time and frequency domain and nonlinear). HRV was compared between those with 3 or more (MetS+) and those with 2 or fewer MetS risk factors (MetS–). Multiple linear regression models were built for each HRV parameter to investigate associations with MetS risk factors and HOMA-IR. Data with normal distribution are presented as means ± SD and those without as median [interquartile range]. In women, standard deviation of R–R intervals 38.0 [27.0] ms, 44.5 [29.3] ms; p = 0.020), low-frequency power (5.73 ± 1.06 ln ms2, 6.13 ± 1.05 ln ms2; p = 0.022), and the standard deviation of the length of the Poincaré plot (46.8 [31.6] ms, 58.4 [29.9] ms; p = 0.014) were lower and heart rate was higher (68 [13] beats/min, 64 [12] beats/min; p = 0. 018) in MetS+ compared with MetS–, with no differences in men. Waist circumference was most commonly associated with HRV, especially frequency domain parameters. HOMA-IR was associated with heart rate. In conclusion, MetS+ women had a less favourable HRV profile than MetS– women, but there were no differences in men. HOMA-IR was associated with heart rate, not HRV.

Depression and anxiety as predictors of heart rate variability after myocardial infarction

2007 ◽  
Vol 38 (3) ◽  
pp. 375-383 ◽  
Author(s):  
E. J. Martens ◽  
I. Nyklíček ◽  
B. M. Szabó ◽  
N. Kupper
Keyword(s):  
Myocardial Infarction ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Anxiety Disorder ◽  
Depressive Disorder ◽  
Anxiety Symptoms ◽  
Adverse Outcomes ◽  
Depression And Anxiety ◽  
Increased Risk

BackgroundReduced heart rate variability (HRV) is a prognostic factor for cardiac mortality. Both depression and anxiety have been associated with increased risk for mortality in cardiac patients. Low HRV may act as an intermediary in this association. The present study examined to what extent depression and anxiety differently predict 24-h HRV indices recorded post-myocardial infarction (MI).MethodNinety-three patients were recruited during hospitalization for MI and assessed on self-reported symptoms of depression and anxiety. Two months post-MI, patients were assessed on clinical diagnoses of lifetime depressive and anxiety disorder. Adequate 24-h ambulatory electrocardiography data were obtained from 82 patients on average 78 days post-MI.ResultsIn unadjusted analyses, lifetime diagnoses of major depressive disorder was predictive of lower SDNN [standard deviation of all normal-to-normal (NN) intervals; β=−0.26, p=0.022] and SDANN (standard deviation of all 5-min mean NN intervals; β=0.25, p=0.023), and lifetime anxiety disorder of lower RMSSD (root mean square of successive differences; β=−0.23, p=0.039). Depression and anxiety symptoms did not significantly predict HRV. After adjustment for age, sex, cardiac history and multi-vessel disease, lifetime depressive disorder was no longer predictive of HRV. Lifetime anxiety disorder predicted reduced high-frequency spectral power (β=−0.22, p=0.039) and RMSSD (β=−0.25, p=0.019), even after additional adjustment of anxiety symptoms.ConclusionsClinical anxiety, but not depression, negatively influenced parasympathetic modulation of heart rate in post-MI patients. These findings elucidate the physiological mechanisms underlying anxiety as a risk factor for adverse outcomes, but also raise questions about the potential role of HRV as an intermediary between depression and post-MI prognosis.

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