scholarly journals Heart Rate Variability Time Analyses of the Streptozotocin- Diabetic Rat

2007 ◽  
Vol 4 (1) ◽  
pp. 64
Author(s):  
M. Jacobson ◽  
F.C. Howarth ◽  
E. Adeghate ◽  
K. Fatima-Shad
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Diabetic Rat ◽  
Physiological Effects ◽  
Short Term ◽  
The World ◽  
Electrocardiogram Ecg ◽  
Prevalence Of Diabetes Mellitus ◽  
Streptozotocin Diabetic Rat

As the world prevalence of diabetes mellitus (DM) increases, animal models of the disease's progression are required for researching effective treatment. The streptozotocin (STZ) treated rat is known to cause hyperglycaemia. This study confirms that this animal model also displays DM physiological effects in the animal heart rate (HR) and heart rate variability (HRV). In particular, 5 minutes of rat (n=13) electrocardiogram (ECG) is acquired hourly for 30 days. At day 10, the animal (n=7) is dosed with STZ and the ECG is analyzed in order to determine the HR and HRV. The HRV is indexed using two time-based analyses, based on long-term (24hr) and short-term (5min) analyses. All analyses are compared to control non-STZ dosed animals (n=6) and display significant DM effects. 

scholarly journals Effect of short-term heart rate variability biofeedback on long-term abstinence in alcohol dependent patients – a one-year follow-up

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Ana Isabel Penzlin ◽  
Kristian Barlinn ◽  
Ben Min-Woo Illigens ◽  
Kerstin Weidner ◽  
Martin Siepmann ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Short Term ◽  
Heart Rate Variability Biofeedback ◽  
One Year ◽  
Alcohol Dependent

scholarly journals Changes in Heart Rate Variability Are Associated with Expression of Short-Term and Long-Term Contextual and Cued Fear Memories

PLoS ONE ◽  
2013 ◽  
Vol 8 (5) ◽  
pp. e63590 ◽  
Author(s):  
Jun Liu ◽  
Wei Wei ◽  
Hui Kuang ◽  
Fang Zhao ◽  
Joe Z. Tsien
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Short Term

scholarly journals Entropy-Based Measures of Hypnopompic Heart Rate Variability Contribute to the Automatic Prediction of Cardiovascular Events

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 241 ◽  
Author(s):  
Xueya Yan ◽  
Lulu Zhang ◽  
Jinlian Li ◽  
Ding Du ◽  
Fengzhen Hou
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Cardiovascular Events ◽  
Predictive Performance ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Short Term ◽  
Cvd Risk ◽  
Term Prediction

Surges in sympathetic activity should be a major contributor to the frequent occurrence of cardiovascular events towards the end of nocturnal sleep. We aimed to investigate whether the analysis of hypnopompic heart rate variability (HRV) could assist in the prediction of cardiovascular disease (CVD). 2217 baseline CVD-free subjects were identified and divided into CVD group and non-CVD group, according to the presence of CVD during a follow-up visit. HRV measures derived from time domain analysis, frequency domain analysis and nonlinear analysis were employed to characterize cardiac functioning. Machine learning models for both long-term and short-term CVD prediction were then constructed, based on hypnopompic HRV metrics and other typical CVD risk factors. CVD was associated with significant alterations in hypnopompic HRV. An accuracy of 81.4% was achieved in short-term prediction of CVD, demonstrating a 10.7% increase compared with long-term prediction. There was a decline of more than 6% in the predictive performance of short-term CVD outcomes without HRV metrics. The complexity of hypnopompic HRV, measured by entropy-based indices, contributed considerably to the prediction and achieved greater importance in the proposed models than conventional HRV measures. Our findings suggest that Hypnopompic HRV assists the prediction of CVD outcomes, especially the occurrence of CVD event within two years.

Abstract P422: Two-kidney One Clip (2k1c) Hypertension Reduces Complexity of Heart Rate Variability in Mice

Hypertension ◽  
2017 ◽  
Vol 70 (suppl_1) ◽  
Author(s):  
Fernanda L Rodrigues ◽  
Luiz E Silva ◽  
Carlos A Silva ◽  
Fernando S Carneiro ◽  
Rita C Tostes ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Multiscale Entropy ◽  
Left Renal Artery ◽  
Fluctuation Analysis ◽  
Detrending Fluctuation Analysis ◽  
Interval Time Series ◽  
Electrocardiogram Ecg ◽  
2K1c Hypertension

Introduction: Hypertension is the most common chronic cardiovascular disease, being multifactorial in origin and an important cause of morbidity and mortality worldwide. Complex behaviors of heart rate series have been widely recognized and the loss of complexity in heart rate variability (HRV) has been shown to predict adverse cardiovascular outcomes. We hypothesized that two-kidney one clip (2K1C) hypertension reduces the HRV complexity in mice. Methods and Results: C57BL/6 mice were anesthetized with isoflurane and submitted to 2K1C hypertension by placing a silver clip (0.12 mm) around left renal artery. After 4 weeks, mice were implanted with subcutaneous electrocardiogram (ECG) electrodes and allowed to recover for 48 h. On the day of the experiment, the ECG was recorded for 30 minutes in conscious, unrestrained mice. At the end of the recording, arterial pressure (AP) was directly measured in each mouse under isoflurane anesthesia. RR interval time series were generated and the complexity of HRV was determined using detrending fluctuation analysis (DFA) and multiscale entropy (MSE). Mean AP was higher in 2K1C mice (133±2 vs 93±4 mmHg) while the HR was similar between groups. DFA scaling exponents were calculated in short (5 to 15), mid (30 to 200) and long (200 to 1500) window sizes, but only the long-term exponent was different between groups (1.27±0.09 vs 0.89±0.08 in 2K1C and sham mice, respectively). MSE was calculated up to scale 20 and averaged in short (1 to 5) and long (6 to 20) time scales. In both short (0.75±0.16 vs 1.25±0.11) and long (0.76±0.17 vs 1.22±0.09) ranges, entropy is lower in hypertensive mice. Conclusions: The complexity of HRV dynamics was found lower in renovascular hypertensive mice. Both sympathetic and vagal control of the heart seems to be involved in this process, as predictability (MSE) and fractality (DFA) is affected in various temporal scales. Nevertheless, the greatest entropy difference between groups is found at scale 6, which is closely related to respiration.

scholarly journals Short-term vs. long-term heart rate variability in ischemic cardiomyopathy risk stratification

2013 ◽  
Vol 4 ◽  
Author(s):  
Andreas Voss ◽  
Rico Schroeder ◽  
Montserrat Vallverdú ◽  
Steffen Schulz ◽  
Iwona Cygankiewicz ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Risk Stratification ◽  
Ischemic Cardiomyopathy ◽  
Short Term

scholarly journals Assessment of autonomic function by long-term heart rate variability: beyond the classical framework of LF and HF measurements

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Junichiro Hayano ◽  
Emi Yuda
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Autonomic Function ◽  
Low Frequency ◽  
Frequency Component ◽  
High Frequency Component ◽  
Short Term ◽  
Heart Rate Turbulence ◽  
Free Running

AbstractIn the assessment of autonomic function by heart rate variability (HRV), the framework that the power of high-frequency component or its surrogate indices reflects parasympathetic activity, while the power of low-frequency component or LF/HF reflects sympathetic activity has been used as the theoretical basis for the interpretation of HRV. Although this classical framework has contributed greatly to the widespread use of HRV for the assessment of autonomic function, it was obtained from studies of short-term HRV (typically 5‑10 min) under tightly controlled conditions. If it is applied to long-term HRV (typically 24 h) under free-running conditions in daily life, erroneous conclusions could be drawn. Also, long-term HRV could contain untapped useful information that is not revealed in the classical framework. In this review, we discuss the limitations of the classical framework and present studies that extracted autonomic function indicators and other useful biomedical information from long-term HRV using novel approaches beyond the classical framework. Those methods include non-Gaussianity index, HRV sleep index, heart rate turbulence, and the frequency and amplitude of cyclic variation of heart rate.

Metabolic Syndrome and Short-Term and Long-Term Heart Rate Variability in Elderly Free of Clinical Cardiovascular Disease: The PROOF Study

2010 ◽  
Vol 13 (6) ◽  
pp. 653-663 ◽  
Author(s):  
H.G. Ntougou Assoumou ◽  
V. Pichot ◽  
J.C. Barthelemy ◽  
V. Dauphinot ◽  
S. Celle ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Metabolic Syndrome ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Short Term

scholarly journals A pilot study: Can heart rate variability (HRV) be determined using short-term photoplethysmograms?

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2354 ◽  
Author(s):  
Mohamed Elgendi ◽  
Ian Norton ◽  
Matt Brearley ◽  
Socrates Dokos ◽  
Derek Abbott ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Negative Relationship ◽  
Short Term ◽  
Post Exercise ◽  
Rate Analysis ◽  
Hot Environment ◽  
Heart Rate Analysis ◽  
The Difference ◽  
Electrocardiogram Ecg

To date, there have been no studies that investigate the independent use of the photoplethysmogram (PPG) signal to determine heart rate variability (HRV). However, researchers have demonstrated that PPG signals offer an alternative way of measuring HRV when electrocardiogram (ECG) and PPG signals are collected simultaneously. Based on these findings, we take the use of PPGs to the next step and investigate a different approach to show the potential independent use of short 20-second PPG signals collected from healthy subjects after exercise in a hot environment to measure HRV. Our hypothesis is that if the PPG--HRV indices are negatively correlated with age, then short PPG signals are appropriate measurements for extracting HRV parameters. The PPGs of 27 healthy male volunteers at rest and after exercise were used to determine the HRV indices: standard deviation of heartbeat interval (SDNN) and the root-mean square of the difference of successive heartbeats (RMSSD). The results indicate that the use of the $aa$ interval, derived from the acceleration of PPG signals, is promising in determining the HRV statistical indices SDNN and RMSSD over 20-second PPG recordings. Moreover, the post-exercise SDNN index shows a negative correlation with age. There tends to be a decrease of the PPG--SDNN index with increasing age, whether at rest or after exercise. This new outcome validates the negative relationship between HRV in general with age, and consequently provides another evidence that short PPG signals have the potential to be used in heart rate analysis without the need to measure lengthy sequences of either ECG or PPG signals.

scholarly journals Reproducibility of Heart Rate Variability Revealed by Repeated Measurements during and after Hemodialysis

2019 ◽  
Vol 49 (3) ◽  
pp. 356-363
Author(s):  
Kerstin Deussing ◽  
Ralph Wendt ◽  
Ronald Burger ◽  
Maik Gollasch ◽  
Joachim Beige
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time ◽  
High Frequency ◽  
Low Frequency ◽  
Repeated Measurements ◽  
Autonomous Nervous System ◽  
Short Term ◽  
Real Time Analysis

Background/Aims: Trajectory of heart rate variability (HRV) represents a noninvasive real-time measure of autonomous nervous system (ANS) and carries the capability of providing new insights into the hemodynamic compensation reserve during hemodialysis (HD). However, studies on HRV reproducibility during HD are scarce and did not refer to different reading periods. In this observational study, we aimed to establish the best suited and most reliable and reproducible HRV index in routine HD treatments including different reading rates. Methods: HRV was characterized by standardized mathematical variation expressions of R/R’ intervals: SD of all R/R’ intervals (ms), square root of the root mean square of the sum of all differences between adjacent R/R’ intervals (ms), percentage of consecutive R/R’ intervals that differ by >50 ms (%), low-frequency spectral analysis HRV (LF, expressing sympathetic activity), and high-frequency HRV (HF, expressing parasympathetic activity). To compare robustness of these HRV indices during HD procedures, we compared HRV indices means between different HD sessions and controlled for association with clinical parameters. Results: In 72 HD treatments of 34 patients, we detected the highest reproducibility (89%) of HRV measures when analyzing the low-frequency to high-frequency (LF/HF) ratio in long-term (3 h) readings. Long-term LF/HF was able to discriminate ­between patients with and without heart failure NYHA classes ≥3 (p = 0.009) and type 2 diabetes (p = 0.023). We were unable to study relationships between ANS and intradialytic complications because they did not appear in our cohort. Short-term readings of HRV indices did not show any significance of pattern change during HD. Conclusion: In summary, our data provide evidence for high robustness of long-term LF/HF in analyzing HRV in HD patients using future automated monitoring systems. For short-term analysis, mathematical real-time analysis must evolve.

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