scholarly journals Heart Rate Variability Assessment by the Lyapunov Exponent

2020 ◽  
Vol 9 (8) ◽  
pp. 724-728
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Lyapunov Exponent ◽  
Low Frequency ◽  
Approximate Entropy ◽  
Mean Value ◽  
Spectral Estimates ◽  
Power Spectral ◽  
Hrv Analysis ◽  
Entropy Estimates

Heart rate variability (HRV) is a measure that evaluates cardiac autonomic activity according to the complexity or irregularity of an HRV dataset. At present, among various entropy estimates, the Lyapunov exponent (LE) is not as well described as approximate entropy (ApEn) and sample entropy (SampEn). Therefore, in this study, we investigated the characteristics of the parameters associated with the LE to evaluate whether the LE parameters can replace the frequency-domain parameters for HRV analysis. For the LE analysis in this study, two-dimensional factors were adjusted: length, which determines the size of the dimension vectors and is known as time delay embedding, varied over a range of 1 to 7, and the interval, which determines the distance between two successive embedding vectors, varied over a range of 1 to 3. A new parameter similar to the LA, the accumulation of the LE, was developed along with the LE to characterize the HRV parameters. The high frequency (HF) components dominated when the mean value of the LA was largest for interval 2, with 2.89 ms2 at the low frequency (LF) and 4.32 ms2 at the HF. The root mean square of the successive difference (RMSSD) in the LE decreased with increasing length in interval 1 from 2.6056 for length 1 to 0.2666 for length 7, resulting in a low HRV. The results suggest that the Lyapunov exponent methodology could be used in characterizing HRV analysis and replace power spectral estimates, specifically, HF components.

Sleep Stage Assessment Using Power Spectral Indices of Heart Rate Variability With a Simple Algorithm

2012 ◽  
Vol 15 (3) ◽  
pp. 264-272 ◽  
Author(s):  
Keiko Tanida ◽  
Masashi Shibata ◽  
Margaret M. Heitkemper
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Low Frequency ◽  
Simple Algorithm ◽  
Sleep Stages ◽  
Spectral Indices ◽  
Current Form ◽  
Power Spectral

Clinical researchers do not typically assess sleep with polysomnography (PSG) but rather with observation. However, methods relying on observation have limited reliability and are not suitable for assessing sleep depth and cycles. The purpose of this methodological study was to compare a sleep analysis method based on power spectral indices of heart rate variability (HRV) data to PSG. PSG and electrocardiography data were collected synchronously from 10 healthy women (ages 20–61 years) over 23 nights in a laboratory setting. HRV was analyzed for each 60-s epoch and calculated at 3 frequency band powers (very low frequency [VLF]-hi: 0.016–0.04 Hz; low frequency [LF]: 0.04–0.15 Hz; and high frequency [HF]: 0.15–0.4 Hz). Using HF/(VLF-hi + LF + HF) value, VLF-hi, and heart rate (HR) as indices, an algorithm to categorize sleep into 3 states (shallow sleep corresponding to Stages 1 & 2, deep sleep corresponding to Stages 3 & 4, and rapid eye movement [REM] sleep) was created. Movement epochs and time of sleep onset and wake-up were determined using VLF-hi and HR. The minute-by-minute agreement rate with the sleep stages as identified by PSG and HRV data ranged from 32 to 72% with an average of 56%. Longer wake after sleep onset (WASO) resulted in lower agreement rates. The mean differences between the 2 methods were 2 min for the time of sleep onset and 6 min for the time of wake-up. These results indicate that distinguishing WASO from shallow sleep segments is difficult using this HRV method. The algorithm's usefulness is thus limited in its current form, and it requires additional modification.

Dynamics of spectral components of heart rate variability during changes in autonomic balance

1998 ◽  
Vol 275 (1) ◽  
pp. H213-H219 ◽  
Author(s):  
Michael V. Højgaard ◽  
Niels-Henrik Holstein-Rathlou ◽  
Erik Agner ◽  
Jørgen K. Kanters
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Supine Position ◽  
Low Frequency ◽  
Power Spectral Analysis ◽  
Frequency Domain Analysis ◽  
Autonomic Balance ◽  
Power Spectral ◽  
Normalized Units ◽  
Semiquantitative Method

Frequency domain analysis of heart rate variability (HRV) has been proposed as a semiquantitative method for assessing activities in the autonomic nervous system. We examined whether absolute powers, normalized powers, and the low frequency-to-high frequency ratio (LF/HF) derived from the HRV power spectrum could detect shifts in autonomic balance in a setting with low sympathetic nervous tone. Healthy subjects were examined for 3 h in the supine position during 1) control conditions ( n = 12), 2) acute β-blockade ( n = 11), and 3) chronic β-blockade ( n = 10). Heart rate fell during the first 40 min of the control session (72 ± 2 to 64 ± 2 beats/min; P < 0.005) and was even lower during acute and chronic β-blockade (56 ± 2 beats/min; P < 0.005). The powers of all spectral areas rose during the first 60 min in all three settings, more so with β-blockade ( P < 0.05). LF/HF was found to contain the same information as powers expressed in normalized units. LF/HF detected the shift in autonomic balance induced by β-blockade but not the change induced by supine position. In conclusion, none of the investigated measures derived from power spectral analysis comprehensively and consistently described the changes in autonomic balance.

Effect of provocative maneuvers on heart rate variability in subjects with quadriplegia

1995 ◽  
Vol 268 (6) ◽  
pp. H2239-H2245 ◽  
Author(s):  
D. R. Grimm ◽  
R. E. DeMeersman ◽  
R. P. Garofano ◽  
A. M. Spungen ◽  
W. A. Bauman
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sympathetic Activity ◽  
Spectral Component ◽  
Low Frequency ◽  
Power Spectral Analysis ◽  
Parasympathetic Activity ◽  
Power Spectral ◽  
Spectral Components ◽  
Male Subjects

This study investigated heart rate variability (HRV) in individuals with quadriplegia who have disruption of autonomic control of the heart. Seven male subjects with neurological complete quadriplegia and seven with incomplete quadriplegia were studied at rest and during provocation. HRV was measured by power spectral analysis using a fast Fourier transform. Two spectral components were generated: 1) the high-frequency (HF) peak, a reflection of parasympathetic activity, and 2) the low-frequency (LF) peak, primarily sympathetic activity with some parasympathetic input. Results of the provocative maneuvers were grouped into one composite variable. Significant differences in the LF spectral component were found between the groups with complete and incomplete lesions in the supine position and after provocation (LF supine: P = 0.01; LF provocation: P = 0.002). After provocation, significant differences were demonstrated in the HF spectral component between these groups (P = 0.005). In contrast to previous findings, a LF component in subjects with complete quadriplegia was observed; this LF component decreased after provocation, suggesting the parasympathetic component withdrew during stressful maneuvers. There also appeared to be general downregulation of parasympathetic activity to the heart in subjects with complete quadriplegia. The presence of an increased LF spectral component during provocation in those with incomplete lesions implies sympathetic stimulation of the heart and may be used as a marker of sympathetic activity in individuals with quadriplegia.

The Comparisons of Heart Rate Variability and Perceived Exertion During Simulated Cycling with Various Viewing Devices

2008 ◽  
Vol 17 (6) ◽  
pp. 575-583 ◽  
Author(s):  
Shih-Fong Huang ◽  
Po-Yi Tsai ◽  
Wen-Hsu Sung ◽  
Chih-Yung Lin ◽  
Tien-Yow Chuang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Perceived Exertion ◽  
Low Frequency ◽  
Control Group ◽  
Threshold Intensity ◽  
Power Spectral ◽  
Significant Difference

Sympathovagal modulation during immersion in a virtual environment is an important influence on human performance of a task. The aim of this study is to investigate sympathovagal modulation using heart rate variability and perceived exertion during exercise in a virtual reality (VR) environment. Sixteen young healthy volunteers were tested while using a stationary bicycle and maintained at an anaerobic threshold intensity for exercise sessions of approximately 10 min duration. Four randomized viewing alternatives were provided including desktop monitor, projector, head mounted device (HMD), and no simulation display. The “no simulation display” served as the control group. A quick ramp exercise test was conducted and maintained at an anaerobic threshold intensity for each session to evaluate power spectral density and rating of perceived exertion (RPE). The sampled heart rate data were rearranged by cubic spline interpolation into power spectrums spanning the ultra-low frequency (ULF) to high frequency (HF) range. A significant difference was found between the no-display and projector groups for total power (TP) and very low frequency (VLF) components. In particular, there was a significant difference when comparing HMD and no-display exercise RPE curves within 6 min of cycling and at the termination of the exercise. A significant difference was also achieved in projector vs. control group comparison at the termination of the exercise. Our results indicate that the use of HMD and the projected VR during cycling can reduce the TP and VLF power spectral density through a proposed decrease in the renin-angiotensin system, with the implication that this humoral effect may enable anaerobic exercise for longer durations through a reduction in sympathetic tone and subsequent increased blood flow to the muscles.

scholarly journals Effect of long-term training on heart rate variability, central hemodynamics and physical working capacity in female swimmers with different sports qualifications

2021 ◽  
Vol 23 (5) ◽  
Author(s):  
Ye. L. Mykhaliuk ◽  
V. V. Syvolap ◽  
Ye. Yu. Horokhovskyi
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Cardiac Activity ◽  
Mean Value ◽  
Stress Index ◽  
Physical Working Capacity ◽  
Central Hemodynamics ◽  
Working Capacity ◽  
Power Spectral

The aim of this study was to compare the indices of heart rate variability, central hemodynamics and physical working capacity in female swimmers with different sports qualifications. Materials and methods. The indices of heart rate variability (HRV), central hemodynamics (CH) and physical development (PD) were studied in 44 female swimmers (mean age 15.00 ± 0.36 years, swimming experience – 7.40 ± 0.35 years) depending on their sports qualifications (MSIC, MS, CMS, first- and second-class athletes). To analyze the autonomic regulation of cardiac activity, power spectral and time-domain indices of HRV were used. CH were examined by the method of automated tetrapolar rheography according to W. Kubiček et al. (1970) in Y. T. Pushkar’s et al. modification (1970). Physical working capacity was measured according to the generally accepted technique on a cycling ergometer using the PWC170 submaximal test. The functional state index (FSI) was calculated using the formula patented by authors. Results. Significant differences were found between the indices of HRV, CH and PD in female swimmers with different qualifications. Thus, in the athletes with the MSIC–MS sports qualifications, heart rate was 61.0 ± 3.8 bpm, cardiac index (CI) – 2.978 ± 0.098 L·min-1·m-2 (there was a trend towards the eukinetic type of hemodynamics (TH)), stress index (SI) – 51.16 ± 12.66 relative units (r.u.), PWC170/kg – 16.98 ± 1.22 kgm·min-1·kg-1, FSI – 6.511 ± 0.422 r.u. A decrease in heart rate among them was correlated with a decrease in SI, and an increase in Mo – with a decrease in CI. In female CMS swimmers, heart rate was 61.37 ± 2.83 beats/min-1, CI – 3.021 ± 0.112 l -1min -1·m -2 , a trend towards the predominantly eukinetic TH, SI – 53.73 ± 9.41 r.u., PWC170 /kg– 14.66 ± 0.683 kgm·min-1·kg -1, FSI – 5.683 ± 0.324 r.u. Reduced values of SI and CI were associated with increased values of Mo and PWC170/kg. In first- and second-class female swimmers, heart rate was 63.05 ± 2.22 beats/min, SI – 50.62 ± 6.4 r.u. This group tended to be eytonic and eukinetic. The mean value of the PWC170/kg was 14.19 ± 0.589 kgm·min-1·kg-1 and FSI – 5.953 ± 0.337 r.u. Correlation analysis confirmed the relationship between the decrease in heart rate and CI and the increase in Mo and PWC170/kg. Conclusions. Long-term training in female swimmers at the distance of 50 to 200 meters is accompanied by the significant increase in the PWC170/kg values with qualification improving, 14.19 ± 0.589 kgm·min-1·kg-1, 14.66 ± 0.683 kgm·min-1·kg-1; 16.98 ± 1.22 kgm·min-1·kg-1, respectively, improvements in HRV (decrease in stress index and increase in Mo) and decrease in CI.

scholarly journals Heart Rate Variability Assessment by the Entropy Parameters during Sleep

2020 ◽  
Vol 9 (9) ◽  
pp. 468-472
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Eye Movement ◽  
Discrimination Performance ◽  
Conditional Entropy ◽  
Approximate Entropy ◽  
Mean Value ◽  
Sleep Stages ◽  
The Mean

The nonlinear heart rate variability (HRV) parameter quantifies autonomic nervous system (ANS) activity based on the complexity or irregularity of an HRV dataset. At present, among various entropy-related parameters during sleep, approximate entropy (ApEn) and sample entropy (SampEn) are not as well understood as other entropy parameters such as Shannon entropy (SE) and conditional entropy (CE). Therefore, in this study, we investigated the characteristics of ApEn and SampEn to differentiate a rapid eye movement (REM) and nonrapid eye movement (NREM) for sleep stages. For nonlinear sleep HRV analysis, two target 10-minute, long-term HRV segments were obtained from each REM and NREM for 16 individual subjects. The target HRV segment was analyzed by moving the 2-minute window forward by 2 s, resulting in 240 results of each ApEn and SampEn. The ApEn and SampEn were averaged to obtain the mean value and standard deviation (SD) of all the results. SampEn provides excellent discrimination performance between REM and NREM in terms of the mean and SD (p<0.0001 and p=0.1989, respectively; 95% CI), but ApEn was inferior to SampEn (p=0.1980 and p=0.9931). The results indicate that SampEn, but not ApEn could be used to discriminate REM from NREM and detect various sleep-related incidents.

Abstract WP301: Noninvasive Beat-to-Beat Assessment of Blood Pressure Variability, Heart Rate Variability and Baroreflex Sensitivity: Which Parameters Best Predict Clinical Outcome After Acute Ischemic Stroke?

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Li Xiong ◽  
Ge Tian ◽  
Xiangyan Chen ◽  
Howan Leung ◽  
Thomas Leung ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Ischemic Stroke ◽  
Clinical Outcome ◽  
Blood Pressure Variability ◽  
Baroreflex Sensitivity ◽  
Low Frequency ◽  
Prognostic Information ◽  
Power Spectral

Background and Objectives: Blood pressure variability (BPV), heart rate variability (HRV) and baroreflex sensitivity (BRS) as measures of autonomic function might provide prognostic information in ischemic stroke. We aimed to study noninvasive beat-to-beat assessment of BPV, HRV and BRS in the acute phase of ischemic stroke to determine whether any of them predicted clinical outcome. Methods & Patients: Consecutive ischemic stroke patients within 7 days of symptom onset were enrolled. The frequency components of BPV and HRV by means of power spectral analysis [very low frequency (VLF; < 0.04 Hz); low frequency (LF; 0.04-0.15 Hz); high frequency (HF; 0.15-0.40 Hz); power spectral density (PSD; <0.40 Hz) and LF/HF ratio] were calculated from 10-minute recordings of beat-to-beat blood pressure and heart rate monitoring. The baroreflex slope and baroreflex effectiveness index (BEI) were determined using the sequence method for BRS. Clinical outcome was assessed at 3 months after stroke onset as good or poor by modified Rankin Scale (mRS) (good outcome, mRS ≤ 2). Results: 82 patients were recruited (mean age, 64.6 ± 9.9 years; 89.3% males). Univariate analysis showed that there were significant differences in National Institutes of Health Stroke Scale (NIHSS) at recruitment, VLF diastolic BPV, VLF, HF and PSD systolic BPV, and down ramp BEI between the good and poor outcome groups (all P < 0.05). After adjusting for NIHSS, multivariate logistic regression showed that only HF systolic BPV (OR 1.320; 95% CI, 1.050-1.659; P=0.017) and down BEI (OR 0.950; 95% CI, 0.912-0.990; P=0.014) were independently correlated with poor functional outcome. Conclusions: Beat-to beat highly variable systolic blood pressure and impaired BRS as evaluated by decreased down BEI are associated with an unfavorable functional outcome after acute ischemic stroke. Important prognostic information can be readily obtained from a short period of noninvasive hemodynamics monitoring in the acute stroke patient.

NONLINEAR HEART RATE VARIABILITY-BASED ANALYSIS AND PREDICTION OF PERFORMANCE STATUS IN PULMONARY METASTASES PATIENTS

2018 ◽  
Vol 30 (06) ◽  
pp. 1850043
Author(s):  
Reema Shyamsunder Shukla ◽  
Yogender Aggarwal
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulmonary Metastases ◽  
Performance Status ◽  
Eastern Cooperative Oncology Group ◽  
Approximate Entropy ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Nonlinear Features ◽  
Hrv Analysis

Cancer causes chronic stress and is associated with impaired autonomic nervous system (ANS). Heart rate variability (HRV) has been suggested to be an important tool in the identification and prediction of performance status (PS) in cancer. Lead II surface electrocardiogram (ECG) was recorded from 24 pulmonary metastases (PM) subjects and 30 healthy controls for nonlinear HRV analysis. Artificial neural network (ANN) and support vector machine (SVM) were applied for the prediction analysis. Analysis of variance (ANOVA) along with post-hoc Tukey’s HSD test was conducted using statistical R, 64-bit, v.3.3.2, at [Formula: see text]. The obtained results suggested lower HRV that increases with cancer severity from the Eastern Cooperative Oncology Group (ECOG)1 PS to ECOG4 PS. ANOVA results stated that approximate entropy (ApEn) ([Formula: see text]-[Formula: see text], [Formula: see text]), detrended fluctuation analysis (DFA) [Formula: see text] ([Formula: see text]-[Formula: see text], [Formula: see text]) and correlation dimension (CD) ([Formula: see text]-[Formula: see text], [Formula: see text]) were significant. The 13 nonlinear features were fed to ANN and SVM to obtain 82.25% and 100% accuracies, respectively. Nonlinear HRV analysis has given promising results in the prediction of diagnosis of PS in PM patients. These inputs would be very useful for clinicians to diagnose PS in their cancer patients and improve their quality of living.

Short-term heart rate variability (HRV) in healthy dogs

2015 ◽  
Vol 18 (2) ◽  
pp. 307-312 ◽  
Author(s):  
Sz. Bogucki ◽  
A. Noszczyk-Nowak
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Reference Values ◽  
Low Frequency ◽  
Total Power ◽  
Short Term ◽  
Ventricular Premature Complexes ◽  
Healthy Dogs ◽  
Hrv Analysis ◽  
Fixed Times

AbstractHeart rate variability (HRV) is a well established mortality risk factor in both healthy dogs and those with heart failure. While the standards for short-term HRV analysis have been developed in humans, only reference values for HRV parameters determined from 24-hour ECG have been proposed in dogs. The aim of this study was to develop the reference values for short-term HRV parameters in a group of 50 healthy dogs of various breeds (age 4.86 ± 2.74 years, body weight 12.2 ± 3.88 kg). The ECG was recorded continuously for at least 180 min in a dark and quiet room. All electrocardiograms were inspected automatically and manually to eliminate atrial or ventricular premature complexes. Signals were transformed into a spectrum using the fast Fourier transform. The HRV parameters were measured at fixed times from 60-min ECG segments. The following time-domain parameters (ms) were analyzed: mean NN, SDNN, SDANN, SDNN index, rMSSD and pNN50. Moreover, frequency-domain parameters (Hz) were determined, including very low frequency (VLF), low frequency (LF) and high frequency (HF) components, total power (TP) and the LF/HF ratio. The results (means ± SD) were as follows: mean NN = 677.68 ± 126.89; SDNN = 208.86 ± 77.1; SDANN = 70.75 ± 30.9; SDNN index = 190.75 ± 76.12; rMSSD = 259 ± 120.17, pNN50 = 71.84 ± 13.96; VLF = 984.96 ± 327.7; LF = 1501.24 ± 736.32; HF = 5845.45 ± 2914.20; TP = 11065.31 ± 3866.87; LF/HF = 0.28 ± 0.11.

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