scholarly journals Nonlinearities of heart rate variability in animal models of impaired cardiac control: contribution of different time scales

2017 ◽  
Vol 123 (2) ◽  
pp. 344-351 ◽  
Author(s):  
Luiz Eduardo Virgilio Silva ◽  
Renata Maria Lataro ◽  
Jaci Airton Castania ◽  
Carlos Alberto Aguiar Silva ◽  
Helio Cesar Salgado ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Multiscale Entropy ◽  
Time Frequency ◽  
Cardiac Control ◽  
Long Time ◽  
Nonlinear Features ◽  
Short Time

Heart rate variability (HRV) has been extensively explored by traditional linear approaches (e.g., spectral analysis); however, several studies have pointed to the presence of nonlinear features in HRV, suggesting that linear tools might fail to account for the complexity of the HRV dynamics. Even though the prevalent notion is that HRV is nonlinear, the actual presence of nonlinear features is rarely verified. In this study, the presence of nonlinear dynamics was checked as a function of time scales in three experimental models of rats with different impairment of the cardiac control: namely, rats with heart failure (HF), spontaneously hypertensive rats (SHRs), and sinoaortic denervated (SAD) rats. Multiscale entropy (MSE) and refined MSE (RMSE) were chosen as the discriminating statistic for the surrogate test utilized to detect nonlinearity. Nonlinear dynamics is less present in HF animals at both short and long time scales compared with controls. A similar finding was found in SHR only at short time scales. SAD increased the presence of nonlinear dynamics exclusively at short time scales. Those findings suggest that a working baroreflex contributes to linearize HRV and to reduce the likelihood to observe nonlinear components of the cardiac control at short time scales. In addition, an increased sympathetic modulation seems to be a source of nonlinear dynamics at long time scales. Testing nonlinear dynamics as a function of the time scales can provide a characterization of the cardiac control complementary to more traditional markers in time, frequency, and information domains. NEW & NOTEWORTHY Although heart rate variability (HRV) dynamics is widely assumed to be nonlinear, nonlinearity tests are rarely used to check this hypothesis. By adopting multiscale entropy (MSE) and refined MSE (RMSE) as the discriminating statistic for the nonlinearity test, we show that nonlinear dynamics varies with time scale and the type of cardiac dysfunction. Moreover, as complexity metrics and nonlinearities provide complementary information, we strongly recommend using the test for nonlinearity as an additional index to characterize HRV.

scholarly journals Multiscale entropy analysis of heart rate variability in heart failure, hypertensive, and sinoaortic-denervated rats: classical and refined approaches

2016 ◽  
Vol 311 (1) ◽  
pp. R150-R156 ◽  
Author(s):  
Luiz Eduardo Virgilio Silva ◽  
Renata Maria Lataro ◽  
Jaci Airton Castania ◽  
Carlos Alberto Aguiar da Silva ◽  
Jose Fernando Valencia ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Complexity Analysis ◽  
Respiratory Control ◽  
Cardiovascular Regulation ◽  
Multiscale Entropy ◽  
Conscious Animal ◽  
Long Time

The analysis of heart rate variability (HRV) by nonlinear methods has been gaining increasing interest due to their ability to quantify the complexity of cardiovascular regulation. In this study, multiscale entropy (MSE) and refined MSE (RMSE) were applied to track the complexity of HRV as a function of time scale in three pathological conscious animal models: rats with heart failure (HF), spontaneously hypertensive rats (SHR), and rats with sinoaortic denervation (SAD). Results showed that HF did not change HRV complexity, although there was a tendency to decrease the entropy in HF animals. On the other hand, SHR group was characterized by reduced complexity at long time scales, whereas SAD animals exhibited a smaller short- and long-term irregularity. We propose that short time scales (1 to 4), accounting for fast oscillations, are more related to vagal and respiratory control, whereas long time scales (5 to 20), accounting for slow oscillations, are more related to sympathetic control. The increased sympathetic modulation is probably the main reason for the lower entropy observed at high scales for both SHR and SAD groups, acting as a negative factor for the cardiovascular complexity. This study highlights the contribution of the multiscale complexity analysis of HRV for understanding the physiological mechanisms involved in cardiovascular regulation.

scholarly journals Multiscale Information Decomposition Dissects Control Mechanisms of Heart Rate Variability at Rest and During Physiological Stress

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 526 ◽  
Author(s):  
Jana Krohova ◽  
Luca Faes ◽  
Barbora Czippelova ◽  
Zuzana Turianikova ◽  
Nikoleta Mazgutova ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Information Transfer ◽  
Physiological Stress ◽  
Short Time Scale ◽  
Control Mechanisms ◽  
Sinus Arrhythmia ◽  
Long Time ◽  
Short Time

Heart rate variability (HRV; variability of the RR interval of the electrocardiogram) results from the activity of several coexisting control mechanisms, which involve the influence of respiration (RESP) and systolic blood pressure (SBP) oscillations operating across multiple temporal scales and changing in different physiological states. In this study, multiscale information decomposition is used to dissect the physiological mechanisms related to the genesis of HRV in 78 young volunteers monitored at rest and during postural and mental stress evoked by head-up tilt (HUT) and mental arithmetics (MA). After representing RR, RESP and SBP at different time scales through a recently proposed method based on multivariate state space models, the joint information transfer T RESP , SBP → RR is decomposed into unique, redundant and synergistic components, describing the strength of baroreflex modulation independent of respiration ( U SBP → RR ), nonbaroreflex ( U RESP → RR ) and baroreflex-mediated ( R RESP , SBP → RR ) respiratory influences, and simultaneous presence of baroreflex and nonbaroreflex respiratory influences ( S RESP , SBP → RR ), respectively. We find that fast (short time scale) HRV oscillations—respiratory sinus arrhythmia—originate from the coexistence of baroreflex and nonbaroreflex (central) mechanisms at rest, with a stronger baroreflex involvement during HUT. Focusing on slower HRV oscillations, the baroreflex origin is dominant and MA leads to its higher involvement. Respiration influences independent on baroreflex are present at long time scales, and are enhanced during HUT.

Time-frequency analysis of heart rate variability using short-time Fourier analysis

2000 ◽  
Vol 21 (2) ◽  
pp. 229-240 ◽  
Author(s):  
Sigrid Elsenbruch ◽  
Zhishun Wang ◽  
William C Orr ◽  
J D Z Chen
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Fourier Analysis ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Short Time

scholarly journals Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 812
Author(s):  
Shiliang Shao ◽  
Ting Wang ◽  
Chunhe Song ◽  
Xingchi Chen ◽  
Enuo Cui ◽  
...  
Keyword(s):  
Heart Rate ◽  
Obstructive Sleep Apnea ◽  
Heart Rate Variability ◽  
Sleep Apnea ◽  
Frequency Spectrum ◽  
Support Vector ◽  
Time Frequency ◽  
Classifier Selection ◽  
Obstructive Sleep ◽  
Short Time

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder. As an alternative to polysomnography (PSG) for OSA screening, the current automatic OSA detection methods mainly concentrate on feature extraction and classifier selection based on physiological signals. It has been reported that OSA is, along with autonomic nervous system (ANS) dysfunction and heart rate variability (HRV), a useful tool for ANS assessment. Therefore, in this paper, eight novel indices of short-time HRV are extracted for OSA detection, which are based on the proposed multi-bands time-frequency spectrum entropy (MTFSE) method. In the MTFSE, firstly, the power spectrum of HRV is estimated by the Burg–AR model, and the time-frequency spectrum image (TFSI) is obtained. Secondly, according to the physiological significance of HRV, the TFSI is divided into multiple sub-bands according to frequency. Last but not least, by studying the Shannon entropy of different sub-bands and the relationships among them, the eight indices are obtained. In order to validate the performance of MTFSE-based indices, the Physionet Apnea–ECG database and K-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT) classification methods are used. The SVM classification method gets the highest classification accuracy, its average accuracy is 91.89%, the average sensitivity is 88.01%, and the average specificity is 93.98%. Undeniably, the MTFSE-based indices provide a novel idea for the screening of OSA disease.

scholarly journals Physiological Resonance in Empathic Stress: Insights from Nonlinear Dynamics of Heart Rate Variability

2021 ◽  
Vol 18 (4) ◽  
pp. 2081 ◽  
Author(s):  
Estelle Blons ◽  
Laurent M. Arsac ◽  
Eric Grivel ◽  
Veronique Lespinet-Najib ◽  
Veronique Deschodt-Arsac
Keyword(s):  
Nonlinear Dynamics ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Stress Responses ◽  
Social Stress ◽  
Stress Test ◽  
Resonance State ◽  
Trier Social Stress Test ◽  
Resonance Phenomenon ◽  
Multiscale Entropy

Because most humans live and work in populated environments, researchers recently took into account that people may not only experience first-hand stress, but also second-hand stress related to the ability to empathically share another person’s stress response. Recently, researchers have begun to more closely examine the existence of such empathic stress and highlighted the human propensity to physiologically resonate with the stress responses of others. As in case of first-hand stress, empathic stress could be deleterious for health if people experience exacerbated activation of hypothalamic–pituitary–adrenal and autonomic nervous systems. Thus, exploring empathic stress in an observer watching someone else experiencing stress is critical to gain a better understanding of physiological resonance and conduct strategies for health prevention. In the current study, we investigated the influence of empathic stress responses on heart rate variability (HRV) with a specific focus on nonlinear dynamics. Classic and nonlinear markers of HRV time series were computed in both targets and observers during a modified Trier social stress test (TSST). We capitalized on multiscale entropy, a reliable marker of complexity for depicting neurovisceral interactions (brain-to-heart and heart-to-brain) and their role in physiological resonance. State anxiety and affect were evaluated as well. While classic markers of HRV were not impacted by empathic stress, we showed that the complexity marker reflected the existence of empathic stress in observers. More specifically, a linear model highlighted a physiological resonance phenomenon. We conclude on the relevance of entropy in HRV dynamics, as a marker of complexity in neurovisceral interactions reflecting physiological resonance in empathic stress.

scholarly journals Multiscale based nonlinear dynamics analysis of heart rate variability signals

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243441
Author(s):  
Syed Zaki Hassan Kazmi ◽  
Nazneen Habib ◽  
Rabia Riaz ◽  
Sanam Shahla Rizvi ◽  
Syed Ali Abbas ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Normal Sinus Rhythm ◽  
Autonomic Control ◽  
Multiscale Entropy ◽  
Permutation Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Cardiac Autonomic Control

Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach multiscale ACI (MACI) is proposed by incorporating multiple time scales for improving the classification ability of ACI. We evaluated the performance of MACI for classifying, normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation subjects. The findings reveal that MACI provided better classification between healthy and pathological subjects compared to ACI. We also compared MACI with other scale-based techniques such as multiscale entropy, multiscale permutation entropy (MPE), multiscale normalized corrected Shannon entropy (MNCSE) and multiscale permutation entropy (IMPE). The preliminary results show that MACI values are more stable and reliable than IMPE and MNCSE. The results show that MACI based features lead to higher classification accuracy.

Does heart rate variability improve prediction of failed extubation in preterm infants?

2019 ◽  
Vol 47 (2) ◽  
pp. 252-257 ◽  
Author(s):  
Marciali Gonçalves Fonseca Silva ◽  
Michele Lima Gregório ◽  
Moacir Fernandes de Godoy
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Preterm Infants ◽  
Approximate Entropy ◽  
Sample Entropy ◽  
Multiscale Entropy ◽  
Statistical Software ◽  
Time Frequency ◽  
Clear Differentiation ◽  
Successful Extubation

Abstract Background Prematurity and its respective comorbidities may result in longer periods of mechanical ventilation in intensive care units (ICU). A method for the assessment of organic maturity would be useful for this population. Heart rate variability (HRV), as an indicator of homeostasis, is a well-established tool for this approach. The objective of the study was to assess HRV in intubated preterm infants in ICU immediately prior to extubation and correlate HRV with clinical evaluation outcomes. Methods A total of 46 preterm infants, 13 (28.2%) males, were prospectively studied and divided into a group with failed extubation (FEG: n=11) and a group with successful extubation (SEG: n=35). HRV was evaluated in time, frequency and nonlinear domains with a Polar RS800 device. HRV measurements were assessed with Kubios HRV Premium Software and statistically analyzed with the StatsDirect Statistical Software, version 1.9.2015 (2002). P<0.05 values were considered as statistically significant. Results There were no significant differences between heart rate variables of failed and successful extubation when analyzing the total group. However, the analysis of the sub-group of preterm infants weighing less than 1000 g showed a clear differentiation between the groups, when the nonlinear variables (approximate entropy, sample entropy and multiscale entropy 1, 2 and 3) were used, demonstrating that the group with successful extubation shows greater complexity and, therefore, relatively greater autonomic stability. Conclusion HRV was effective in predicting failed extubation in preterm infants when evaluated in a nonlinear domain and in preterm infants weighing less than 1000 g.

The Spread of Pollutants from Harbour Sludge Depots

1984 ◽  
Vol 16 (3-4) ◽  
pp. 623-633
Author(s):  
M Loxham ◽  
F Weststrate
Keyword(s):  
Time Scales ◽  
Molecular Diffusion ◽  
Near Field ◽  
Parameter Analysis ◽  
Migration Patterns ◽  
Long Time ◽  
Dilution Effects ◽  
Short Time ◽  
Harbour Sludge

It is generally agreed that both the landfill option, or the civil techniques option for the final disposal of contaminated harbour sludge involves the isolation of the sludge from the environment. For short time scales, engineered barriers such as a bentonite screen, plastic sheets, pumping strategies etc. can be used. However for long time scales the effectiveness of such measures cannot be counted upon. It is thus necessary to be able to predict the long term environmenttal spread of contaminants from a mature landfill. A model is presented that considers diffusion and adsorption in the landfill site and convection and adsorption in the underlaying aquifer. From a parameter analysis starting form practical values it is shown that the adsorption behaviour and the molecular diffusion coefficient of the sludge, are the key parameters involved in the near field. The dilution effects of the far field migration patterns are also illustrated.

Sign in / Sign up

Export Citation Format

Share Document