Depressive Symptoms Are Related to Decreased Low-Frequency Heart Rate Variability in Older Adults with Decompensated Heart Failure

2007 ◽  
Vol 55 (3-4) ◽  
pp. 219-224 ◽  
Author(s):  
Salvador M. Guinjoan ◽  
Mariana N. Castro ◽  
Daniel E. Vigo ◽  
Hylke Weidema ◽  
Carlos Berbara ◽  
...  
Keyword(s):  
Heart Failure ◽  
Older Adults ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Depressive Symptoms ◽  
Low Frequency ◽  
Decompensated Heart Failure

scholarly journals Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study (Preprint)

2020 ◽  
Author(s):  
Natasa Reljin ◽  
Hugo F. Posada-Quintero ◽  
Caitlin Eaton-Robb ◽  
Sophia Binici ◽  
Emily Ensom ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
High Frequency ◽  
Acute Decompensated Heart Failure ◽  
Low Frequency ◽  
Decompensated Heart Failure ◽  
Dynamic Mode ◽  
Fluid Accumulation ◽  
Frequency Components

BACKGROUND Accumulation of excess body fluid and autonomic dysregulation are clinically important characteristics of acute decompensated heart failure. We hypothesized that transthoracic bioimpedance, a noninvasive, simple method for measuring fluid retention in lungs, and heart rate variability, an assessment of autonomic function, can be used for detection of fluid accumulation in patients with acute decompensated heart failure. OBJECTIVE We aimed to evaluate the performance of transthoracic bioimpedance and heart rate variability parameters obtained using a fluid accumulation vest with carbon black–polydimethylsiloxane dry electrodes in a prospective clinical study (System for Heart Failure Identification Using an External Lung Fluid Device; SHIELD). METHODS We computed 15 parameters: 8 were calculated from the model to fit Cole-Cole plots from transthoracic bioimpedance measurements (extracellular, intracellular, intracellular-extracellular difference, and intracellular-extracellular parallel circuit resistances as well as fitting error, resonance frequency, tissue heterogeneity, and cellular membrane capacitance), and 7 were based on linear (mean heart rate, low-frequency components of heart rate variability, high-frequency components of heart rate variability, normalized low-frequency components of heart rate variability, normalized high-frequency components of heart rate variability) and nonlinear (principal dynamic mode index of sympathetic function, and principal dynamic mode index of parasympathetic function) analysis of heart rate variability. We compared the values of these parameters between 3 participant data sets: control (n=32, patients who did not have heart failure), baseline (n=23, patients with acute decompensated heart failure taken at the time of admittance to the hospital), and discharge (n=17, patients with acute decompensated heart failure taken at the time of discharge from hospital). We used several machine learning approaches to classify participants with fluid accumulation (baseline) and without fluid accumulation (control and discharge), termed <i>with fluid and without fluid</i> groups, respectively. RESULTS Among the 15 parameters, 3 transthoracic bioimpedance (extracellular resistance, R<sub>0</sub>; difference in extracellular-intracellular resistance, R<sub>0</sub> – R<sub>∞</sub>, and tissue heterogeneity, α) and 3 heart rate variability (high-frequency, normalized low-frequency, and normalized high-frequency components) parameters were found to be the most discriminatory between groups (patients with and patients without heart failure). R<sub>0</sub> and R<sub>0</sub> – R<sub>∞</sub> had significantly lower values for patients with heart failure than for those without heart failure (R<sub>0</sub>: <i>P</i>=.006; R<sub>0</sub> – R<sub>∞</sub>: <i>P</i>=.001), indicating that a higher volume of fluids accumulated in the lungs of patients with heart failure. A cubic support vector machine model using the 5 parameters achieved an accuracy of 92% for with fluid and without fluid group classification. The transthoracic bioimpedance parameters were related to intra- and extracellular fluid, whereas the heart rate variability parameters were mostly related to sympathetic activation. CONCLUSIONS This is useful, for instance, for an in-home diagnostic wearable to detect fluid accumulation. Results suggest that fluid accumulation, and subsequently acute decompensated heart failure detection, could be performed using transthoracic bioimpedance and heart rate variability measurements acquired with a wearable vest.

scholarly journals Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study

10.2196/18715 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e18715
Author(s):  
Natasa Reljin ◽  
Hugo F Posada-Quintero ◽  
Caitlin Eaton-Robb ◽  
Sophia Binici ◽  
Emily Ensom ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
High Frequency ◽  
Acute Decompensated Heart Failure ◽  
Low Frequency ◽  
Decompensated Heart Failure ◽  
Dynamic Mode ◽  
Fluid Accumulation ◽  
Frequency Components

Background Accumulation of excess body fluid and autonomic dysregulation are clinically important characteristics of acute decompensated heart failure. We hypothesized that transthoracic bioimpedance, a noninvasive, simple method for measuring fluid retention in lungs, and heart rate variability, an assessment of autonomic function, can be used for detection of fluid accumulation in patients with acute decompensated heart failure. Objective We aimed to evaluate the performance of transthoracic bioimpedance and heart rate variability parameters obtained using a fluid accumulation vest with carbon black–polydimethylsiloxane dry electrodes in a prospective clinical study (System for Heart Failure Identification Using an External Lung Fluid Device; SHIELD). Methods We computed 15 parameters: 8 were calculated from the model to fit Cole-Cole plots from transthoracic bioimpedance measurements (extracellular, intracellular, intracellular-extracellular difference, and intracellular-extracellular parallel circuit resistances as well as fitting error, resonance frequency, tissue heterogeneity, and cellular membrane capacitance), and 7 were based on linear (mean heart rate, low-frequency components of heart rate variability, high-frequency components of heart rate variability, normalized low-frequency components of heart rate variability, normalized high-frequency components of heart rate variability) and nonlinear (principal dynamic mode index of sympathetic function, and principal dynamic mode index of parasympathetic function) analysis of heart rate variability. We compared the values of these parameters between 3 participant data sets: control (n=32, patients who did not have heart failure), baseline (n=23, patients with acute decompensated heart failure taken at the time of admittance to the hospital), and discharge (n=17, patients with acute decompensated heart failure taken at the time of discharge from hospital). We used several machine learning approaches to classify participants with fluid accumulation (baseline) and without fluid accumulation (control and discharge), termed with fluid and without fluid groups, respectively. Results Among the 15 parameters, 3 transthoracic bioimpedance (extracellular resistance, R0; difference in extracellular-intracellular resistance, R0 – R∞, and tissue heterogeneity, α) and 3 heart rate variability (high-frequency, normalized low-frequency, and normalized high-frequency components) parameters were found to be the most discriminatory between groups (patients with and patients without heart failure). R0 and R0 – R∞ had significantly lower values for patients with heart failure than for those without heart failure (R0: P=.006; R0 – R∞: P=.001), indicating that a higher volume of fluids accumulated in the lungs of patients with heart failure. A cubic support vector machine model using the 5 parameters achieved an accuracy of 92% for with fluid and without fluid group classification. The transthoracic bioimpedance parameters were related to intra- and extracellular fluid, whereas the heart rate variability parameters were mostly related to sympathetic activation. Conclusions This is useful, for instance, for an in-home diagnostic wearable to detect fluid accumulation. Results suggest that fluid accumulation, and subsequently acute decompensated heart failure detection, could be performed using transthoracic bioimpedance and heart rate variability measurements acquired with a wearable vest.

scholarly journals Depressive Symptoms are Associated with Heart Rate Variability Independently of Fitness: A Cross-Sectional Study of Patients with Heart Failure

2019 ◽  
Vol 53 (11) ◽  
pp. 955-963 ◽  
Author(s):  
Fawn A Walter ◽  
Emily Gathright ◽  
Joseph D Redle ◽  
John Gunstad ◽  
Joel W Hughes
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Depressive Symptoms ◽  
Physical Fitness ◽  
Secondary Analysis ◽  
Small Sample ◽  
Step Test ◽  
Cross Sectional ◽  
The Relationship

Abstract Background Depression is associated with reduced heart rate variability (HRV) in healthy and cardiac samples, which may be accounted for by physical fitness. In a small sample of cardiac patients, activity and fitness levels attenuated the relationship between HRV and depression. In the current study of heart failure (HF) patients, we hypothesized that depressive symptoms and HRV would be inversely related and physical fitness would attenuate this association. Purpose To determine if previous associations among depressive symptoms, physical fitness, and HRV would replicate in a sample of HF patients. Methods The sample consisted of HF patients (N = 125) aged 68.55 ± 8.92 years, 68.8% male, and 83.2% Caucasian. The study was cross-sectional and a secondary analysis of a nonrandomized clinical trial (Trial Identifier: NCT00871897). Depressive symptoms were evaluated using the Beck Depression Inventory (BDI)-II, fitness with the 2 min step test (2MST), and HRV during a 10 min resting laboratory psychophysiology protocol. The dependent variable in hierarchical linear regressions was the root mean square of successive differences. Results Controlling for sex, age, β-blocker use, hypertension, and diabetes, higher BDI-II scores significantly predicted lower HRV, β = −.29, t(92) = −2.79, p < .01. Adding 2MST did not attenuate the relationship in a follow-up regression. Conclusion Depressive symptoms were associated with lower HRV in HF patients, independent of physical fitness. Given the prevalence of depression and suppressed HRV common among HF patients, interventions addressing depressive symptoms and other predictors of poor outcomes may be warranted.

scholarly journals Influence of heart failure severity on heart rate variability

2005 ◽  
Vol 133 (11-12) ◽  
pp. 484-491 ◽  
Author(s):  
Danijela Zamaklar-Trifunovic ◽  
Petar Seferovic ◽  
Mirjana Zivkovic ◽  
Vera Jelic ◽  
Goran Vukomanovic ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Nyha Class ◽  
Low Frequency ◽  
Total Power ◽  
Pump Failure ◽  
Spectral Components ◽  
Failure Severity ◽  
Heart Failure Severity

Introduction. Autonomic regulation of cardiovascular functions in congestive heart failure is characterised by enhanced sympathetic and diminished parasympathetic activity. The long term predominance of sympathetic tone is a significant factor in arrhythmogenesis, sudden cardiac death, and progressive pump failure. Heart rate variability (HRV) is a noninvasive method for estimating the sympatho vagal balance in cardiovascular control. Aim. The aim of this study was to analyse the influence of heart failure severity on HRV. Method. HRV was estimated through the spectral analysis of short term ECG (Cardiovit AT 60, Schiller, CH) in 63 patients (78% male, mean age 56.9?10.9 years) and 14 healthy volunteers (57.1% male, mean age 53.1?8.2 years). The following spectral components were measured: VLF (very low frequency), LF (low frequency), HF (high frequency), and total power (Tot Power). Results. All spectral components were statistically, significantly lower in patients with heart failure in comparison to healthy controls (VLF: 159.89?147.02 vs. 285.50?202.77 ms2; p=0.023, LF: 161.48?204.01 vs. 474.57?362.93 ms2; p<0.001, HF: 88.58?102.47 vs. 362.71?318.28 ms2; p<0.001), as well as total power (Tot Power: 723.39?644.52 vs. 1807.29?1204.74 ms2; p<0.001). A significant, negative correlation between HRV parameters and NYHA class was detected in heart failure patients (VLF: r=-0.391; p=0.002, LF: r=-0.401; p=0.001, and Tot Power r=-0.372; p=0.003). Ejection fraction proved to be in significant, positive correlation to VLF (r=0.541; p=0.002), LF (r=0.531; p=0.003), HF (r=0.418; p=0.020), and Tot Power (r=0.457; p=0.013). Conclusion. Significant HRV reduction is a precursor to incipient heart failure (NYHA I). In heart failure progression, total power as well as the power of all spectral components is progressively reduced. LF and Tot Power are the most prominent parameters for discriminating between the different stages of heart failure. These results could promote HRV as an important decision-making tool in heart failure treatment as well as in monitoring the results of that treatment.

Sympathetic Stimulations by Exercise-Stress Testing and by Dobutamine Infusion Induce Similar Changes in Heart Rate Variability in Patients with Chronic Heart Failure

1995 ◽  
Vol 89 (2) ◽  
pp. 155-164 ◽  
Author(s):  
Massimo Piepoli ◽  
Stamatis Adamopoulos ◽  
Luciano Bernardi ◽  
Peter Sleight ◽  
Andrew J. S. Coats
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Heart Failure ◽  
Physical Exercise ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Component ◽  
Dobutamine Infusion ◽  
Inotropic Therapy

1. Heart rate variability can be used to evaluate autonomic balance, but it is unclear how inotropic therapy may affect the findings. The aim of the study was to assess whether heart rate variability can differentiate between sympathetic stimulation induced by inotrope infusion or by physical exercise. 2. Ten patients with chronic heart failure (64.3 ± 5.4 years of age) underwent four dobutamine infusions (8-min steps of 5 μg min−1 kg−1) and four supine bicycle exercise tests (5-min steps of 25 W). Plasma noradrenaline was evaluated, as well as the SD of R—R intervals, together with low-frequency (0.03–0.14 Hz) and high-frequency (0.15–0.4 Hz) components of heart rate variability using autoregressive spectral analysis. 3. Exercise and inotrope infusion produced similar changes in heart rate variability. An exercise load of 50 W and a dobutamine infusion of 15 μg min−1 kg−1 gave the following results respectively: heart rate, 120.3 ± 3.0 beats/min versus 110.2 ± 3.0 beats/min; SD, 16.0 ± 1.1 ms versus 16.3 ± 2.5 ms; low-frequency component, 4.3 ± 0.3 ln-ms2 versus 4.4 ± 0.3 ln-ms2 and high-frequency component, 2.6 ± 0.3 ln-ms2 versus 2.2 ± 0.3 ln-ms2. All comparisons were non-significant. The variables of heart rate variability showed high reproducibility in the same subject during different conditions. Noradrenaline was elevated by exercise from 326.0 ± 35.2 pg/ml to 860.1 ± 180.4 pg/ml (P < 0.05), but was unchanged by dobutamine infusion. 4. Heart rate variability changes cannot differentiate between dobutamine infusions and physical exercise, indicating that we should be cautious in evaluating patients undergoing inotropic therapy. The degree of receptor stimulations, rather than the level of sympathetic drive, would appear to determine the changes in heart rate variability.

Reproducibility of Heart Rate Variability Measures in Patients with Chronic Heart Failure

1996 ◽  
Vol 91 (4) ◽  
pp. 391-398 ◽  
Author(s):  
Piotr Ponikowski ◽  
Massimo Piepoli ◽  
Aham A. Amadi ◽  
Tuan Peng Chua ◽  
Derek Harrington ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Heart Failure ◽  
Time Domain ◽  
Low Frequency ◽  
Variation Coefficient ◽  
Very Low Frequency ◽  
Low Frequency Power ◽  
Frequency Power

1. In patients with chronic heart failure, heart rate variability is reduced with relative preservation of very-low-frequency power (< 0.04 Hz). Heart rate variability has been measured without acceptable information on its stability and the optimal recording periods for enhancing this reproducibility. 2. To this aim and to establish the optimal length of recording for the evaluation of the very-low-frequency power, we analysed 40, 20, 10 and 5 min ECG recordings obtained on two separate occasions in 16 patients with chronic heart failure. The repeatability coefficient and the variation coefficient were calculated for the heart rate variability parameters, in the time-domain (mean RR, SDRR and pNN50), and in the frequency-domain: very low frequency (< 0.04 Hz), low frequency (0.04–0.15 Hz), high frequency (0.15–0.40 Hz), total power (0–0.5 Hz). 3. Mean RR remained virtually identical over time (variation coefficient 8%). The reproducibility of time-domain (variation coefficient 25–139%) and of spectral measures (variation coefficient 45–111%) was very low. The stability of the heart rate variability parameters was only apparently improved after square root and after log transformation. 4. Very-low-frequency values derived from 5 and 10 min intervals were significantly lower than those calculated from 40 and 20 min intervals (P < 0.005). Discrete very-low-frequency peaks were detected in 11 out of 16 patients on the first 40, 20 and 10 min recording, but only in seven out of 16 when 5 min segments were analysed. 5. The reproducibility of both time or frequency-domain measures of heart rate variability in patients with chronic heart failure may vary significantly. Square root or log-transformed parameters may be considered rather than absolute units in studies assessing the influence of management on heart rate variability profile. Recordings of at least 20 min in stable, controlled conditions are to be recommended to optimize signal acquisition in patients with chronic heart failure, if very-low-frequency power in particular is to be studied.

Persistent alterations in heart rate variability, baroreflex sensitivity, and anxiety-like behaviors during development of heart failure in the rat

2008 ◽  
Vol 295 (1) ◽  
pp. H29-H38 ◽  
Author(s):  
Marcus Henze ◽  
Davin Hart ◽  
Allen Samarel ◽  
John Barakat ◽  
Laurie Eckert ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Baroreflex Sensitivity ◽  
Elevated Plus Maze ◽  
Low Frequency ◽  
Coronary Artery Ligation ◽  
Artery Ligation ◽  
Arterial Blood ◽  
Plus Maze

Depressed heart rate variability and mood are associated with increased mortality in patients with congestive heart failure (CHF). Here autonomic indexes were assessed 3 and 7 wk after left coronary artery ligation in telemetered rats, after which anxiety-like behaviors were assessed in an elevated plus maze. Low frequency (LF) and high frequency (HF) heart rate variability were reduced in CHF rats 3 wk after infarction (LF, 1.60 ± 0.52 vs. 6.97 ± 0.79 ms2; and HF, 1.53 ± 0.39 vs. 6.20 ± 1.01 ms2; P < 0.01). The number of sequences of interbeat intervals that correlated with arterial pressure was decreased in CHF rats at 3 and 7 wk ( week 3, 26.60 ± 10.85 vs. 59.75 ± 11.4 sequences, P < 0.05; and week 7, 20.80 ± 8.97 vs. 65.38 ± 5.89 sequences, P < 0.01). Sequence gain was attenuated in CHF rats by 7 wk (1.34 ± 0.06 vs. 2.70 ± 0.29 ms/mmHg, P < 0.01). Coherence between interbeat interval and mean arterial blood pressure variability in the LF domain was reduced in CHF rats at 3 (0.12 ± 0.03 vs. 0.26 ± 0.05 k2, P < 0.05) and 7 (0.16 ± 0.02 vs. 0.31 ± 0.05 k2, P < 0.05) wk. CHF rats invariably entered the open arm of the elevated plus maze first and spent more time in the open arms (36.0 ± 15% vs. 4.6 ± 1.9%, P < 0.05). CHF rats also showed a tendency to jump head first off the apparatus, whereas controls did not. Together the data indicate that severe autonomic dysfunction is accompanied by escape-seeking behaviors in rats with verified CHF.

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