Frequency modulation between low- and high-frequency components of the heart rate variability spectrum

2006 ◽  
Vol 51 (4) ◽  
pp. 251-254 ◽  
Author(s):  
Yuru Zhong ◽  
Kung-Ming Jan ◽  
Ki H. Chon
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Modulation ◽  
High Frequency ◽  
Frequency Components ◽  
Heart Rate Variability Spectrum

Coarse-graining spectral analysis: new method for studying heart rate variability

1991 ◽  
Vol 71 (3) ◽  
pp. 1143-1150 ◽  
Author(s):  
Y. Yamamoto ◽  
R. L. Hughson
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Spectral Analysis ◽  
High Frequency ◽  
Low Frequency ◽  
Scale Invariant ◽  
Very Low Frequency ◽  
Frequency Components ◽  
Harmonic Components

Heart rate variability (HRV) spectra are typically analyzed for the components related to low- (less than 0.15 Hz) and high- (greater than 0.15 Hz) frequency variations. However, there are very-low-frequency components with periods up to hours in HRV signals, which might smear short-term spectra. We developed a method of spectral analysis suitable for selectively extracting very-low-frequency components, leaving intact the low- and high-frequency components of interest in HRV spectral analysis. Computer simulations showed that those low-frequency components were well characterized by fractional Brownian motions (FBMs). If the scale invariant, or self-similar, property of FBMs is considered a new time series (x′) was constructed by sampling only every other point (course graining) of the original time series (x). Evaluation of the cross-power spectra between these two (Sxx′) showed that the power of the FBM components was preserved, whereas that of the harmonic components vanished. Subtraction of magnitude of Sxx from the autopower spectra of the original sequence emphasized only the harmonic components. Application of this method to HRV spectral analyses indicated that it might enable one to observe more clearly the low- and high-frequency components characteristic of autonomic control of heart rate.

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.

Effect of Inhalation of 30% Nitrous Oxide on Spectral Components of Heart Rate Variability in Conscious man

1993 ◽  
Vol 85 (4) ◽  
pp. 389-392 ◽  
Author(s):  
D. C. Galletly ◽  
P. D. Tobin ◽  
B.J. Robinson ◽  
T. Corfiatis
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Spectrum ◽  
High Frequency ◽  
Low Frequency ◽  
Spectral Components ◽  
Frequency Components ◽  
The Mean ◽  
Frequency Power ◽  
High Frequency Spectrum

1. Periodicities in cardiac interbeat interval may be resolved into discrete frequency components by applying Fourier analysis to heart rate time series. Low-frequency components (<0.15 Hz) are believed to be under parasympathetic and sympathetic control, whereas a higher frequency component in phase with respiration is believed to be entirely parasympathetic. The ratio of the power in the low-/high-frequency spectrum gives an estimate of sympathetic/para-sympathetic balance. 2. This study examined, using heart rate variability spectral analysis, the cardiac autonomic effects of breathing 30% N2O in normal subjects. While supine, the inhalation of N2O caused a significant fall in high-frequency power and a rise in the low-/high-frequency spectrum. During air breathing, tilting caused a significant rise in the mean blood pressure, heart rate, low-frequency power and low-/high-frequency spectrum. During N2O breathing, tilting caused a rise in the heart rate and the mean blood pressure, but no significant alteration in the power of individual spectral components. During tilting, the heart rate, the low-frequency and low-/high-frequency spectrum were less when breathing N2O than when breathing air. 3. These observations are consistent with the effect of N2O being an enhanced sympathetic balance of sinoatrial control, with the primary effect being through reduced parasympathetic tone. Enhanced sympathetic dominance of heart rate variability was seen on standing while subjects breathed air, but this effect was blunted with N2O.

High-Frequency Modulation of Heart Rate Variability During Exercise in Patients With COPD

CHEST Journal ◽  
2003 ◽  
Vol 124 (3) ◽  
pp. 863-869 ◽  
Author(s):  
Matthew N. Bartels ◽  
Sanja Jelic ◽  
Pakkay Ngai ◽  
Robert C. Basner ◽  
Ronald E. DeMeersman
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Modulation ◽  
High Frequency

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 Effect of Hypoglycemia on Heart Rate Variability in People with Type 1 Diabetes and Impaired Awareness of Hypoglycemia

2021 ◽  
pp. 193229682110074
Author(s):  
Mats Koeneman ◽  
Marleen Olde Bekkink ◽  
Lian van Meijel ◽  
Sebastian Bredie ◽  
Bastiaan de Galan
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
High Frequency ◽  
Sympathetic Activity ◽  
Elevated Risk ◽  
Wilcoxon Rank Sum Test ◽  
Impaired Awareness ◽  
Induced Changes ◽  
Measurable Change

Background: People with impaired awareness of hypoglycemia (IAH) are at elevated risk of severe, potentially hazardous, hypoglycemia and would benefit from a device alerting to hypoglycemia. Heart rate variability (HRV) changes with hypoglycemia due to sympathetic activity. Since IAH is associated with suppressed sympathetic activity, we investigated whether hypoglycemia elicits a measurable change in HRV in patients with T1D and IAH. Method: Eligible participants underwent a modified hyperinsulinemic euglycemic hypoglycemic clamp (glucose nadir, 43.1 ± 0.90 mg/dl), while HRV was measured by a VitalConnect HealthPatch. Measurements of HRV included Root Mean Square of the Successive Differences (RMSSD) and low to high frequency (LF:HF) ratio. Wilcoxon rank-sum test was used for testing within-subject HRV changes. Results: We included 12 participants (8 female, mean age 57 ± 12 years, mean HbA1c 57 ± 5 mmol/mol (7.4 ± 0.4%)). Symptoms increased from 4.0 (1.5-7.0) at euglycemia to 7.5 (5.0-11.0) during hypoglycemia ( P = .003). In response to hypoglycemia, the LF:HF ratio and RMSSD increased when normalized for data obtained during euglycemia (both P < .01). The LF:HF ratio increased in 6 participants (50%) and declined in one other participant (8%). The RMSSD decreased in 3 (25%) and increased in 4 (33%) participants. In 2 patients, no change in HRV could be detected in response to hypoglycemia. Conclusions: This study reveals that hypoglycemia-induced changes in HRV are retained in the majority of people with T1D and IAH, and that these changes can be detected by a wearable device. Real-time HRV seems usable for detection of hypoglycemia in patients with IAH.

scholarly journals Effect of short-term exposure to particulate air pollution on heart rate variability in normal-weight and obese adults

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Luyi Li ◽  
Dayu Hu ◽  
Wenlou Zhang ◽  
Liyan Cui ◽  
Xu Jia ◽  
...  
Keyword(s):  
Heart Rate ◽  
Air Pollution ◽  
Heart Rate Variability ◽  
High Frequency ◽  
Normal Weight ◽  
High Frequency Power ◽  
Short Term ◽  
Particulate Air Pollution ◽  
Short Term Exposure ◽  
Frequency Power

Abstract Background The adverse effects of particulate air pollution on heart rate variability (HRV) have been reported. However, it remains unclear whether they differ by the weight status as well as between wake and sleep. Methods A repeated-measure study was conducted in 97 young adults in Beijing, China, and they were classified by body mass index (BMI) as normal-weight (BMI, 18.5–24.0 kg/m2) and obese (BMI ≥ 28.0 kg/m2) groups. Personal exposures to fine particulate matter (PM2.5) and black carbon (BC) were measured with portable exposure monitors, and the ambient PM2.5/BC concentrations were obtained from the fixed monitoring sites near the subjects’ residences. HRV and heart rate (HR) were monitored by 24-h Holter electrocardiography. The study period was divided into waking and sleeping hours according to time-activity diaries. Linear mixed-effects models were used to investigate the effects of PM2.5/BC on HRV and HR in both groups during wake and sleep. Results The effects of short-term exposure to PM2.5/BC on HRV were more pronounced among obese participants. In the normal-weight group, the positive association between personal PM2.5/BC exposure and high-frequency power (HF) as well as the ratio of low-frequency power to high-frequency power (LF/HF) was observed during wakefulness. In the obese group, personal PM2.5/BC exposure was negatively associated with HF but positively associated with LF/HF during wakefulness, whereas it was negatively correlated to total power and standard deviation of all NN intervals (SDNN) during sleep. An interquartile range (IQR) increase in BC at 2-h moving average was associated with 37.64% (95% confidence interval [CI]: 25.03, 51.51%) increases in LF/HF during wakefulness and associated with 6.28% (95% CI: − 17.26, 6.15%) decreases in SDNN during sleep in obese individuals, and the interaction terms between BC and obesity in LF/HF and SDNN were both statistically significant (p <  0.05). The results also suggested that the effects of PM2.5/BC exposure on several HRV indices and HR differed in magnitude or direction between wake and sleep. Conclusions Short-term exposure to PM2.5/BC is associated with HRV and HR, especially in obese individuals. The circadian rhythm of HRV should be considered in future studies when HRV is applied. Graphical abstract

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