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 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 Multiple Time Scales Analysis for Identifying Congestive Heart Failure Based on Heart Rate Variability

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 17862-17871 ◽  
Author(s):  
Baiyang Hu ◽  
Shoushui Wei ◽  
Dingwen Wei ◽  
Lina Zhao ◽  
Guohun Zhu ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Multiple Time Scales ◽  
Multiple Time

Multiscale entropy-based methods for heart rate variability complexity analysis

2015 ◽  
Vol 422 ◽  
pp. 143-152 ◽  
Author(s):  
Luiz Eduardo Virgilio Silva ◽  
Brenno Caetano Troca Cabella ◽  
Ubiraci Pereira da Costa Neves ◽  
Luiz Otavio Murta Junior
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Complexity Analysis ◽  
Multiscale Entropy

USING DIFFERENT ENTROPIES TO ANALYZE THE HEART RATE VARIABILITY OF CONGESTIVE HEART FAILURE PATIENTS

2011 ◽  
Vol 23 (04) ◽  
pp. 253-260 ◽  
Author(s):  
Ren-Guey Lee ◽  
Chun-Chieh Hsiao ◽  
Chieh-Yi Kao
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Analysis Of Variance ◽  
Human Body ◽  
Approximate Entropy ◽  
Young Group ◽  
Multiscale Entropy ◽  
Heart Failure Patients

The purpose of this paper is to show the influence of congestive heart failure (CHF) on heart by using different entropies to apply on the group of patients with CHF and normal group. Three different entropies are used: approximate entropy (ApEn), multiscale entropy (MSE), and base-scale entropy (BsEn). We use these three entropies to measure the complexity of the heart rate variability (HRV) and also use analysis of variance (ANOVA) to analyze the result of entropies to discuss the feasibility of recognizing CHF patients by utilizing entropies. With the analysis results of different entropies, the influence of CHF on heart has also been clearly demonstrated. The results on the approximate entropy show that the normal young group has a higher approximate entropy value while the CHF group has a lower value. This can be explained as a healthy, strong heart that can change its heart rate freely to adapt the change of the environment or the needs of the human body, therefore the HRV will be more complex. From the ANOVA results of approximate entropy, it can be observed that the F value is larger than 1, but is still small. In other words, the approximate entropy can be used to distinguish the three groups, the effect is, however, not good. It is hard to recognize a CHF patient by using approximate entropy.

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.

Long-term Correlations and Complexity Analysis of the Heart Rate Variability Signal during Sleep

2010 ◽  
Vol 49 (05) ◽  
pp. 479-483 ◽  
Author(s):  
M. O. Mendez ◽  
M. Ferrario ◽  
L. Ferini-Strambi ◽  
S. Cerutti ◽  
A. M. Bianchi
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Disorders ◽  
Healthy Subjects ◽  
Complexity Analysis ◽  
Sleep Efficiency ◽  
Heart Failure Patients ◽  
Obstructive Sleep

Summary Background: Physiological sleep is characterized by different cyclic phenomena, such as REM, nonREM phases and the Cyclic Alternating Pattern (CAP), that are associated to characteristic patterns in the heart rate variability (HRV) signal. Disruption of such rhythms due to sleep disorders, for example insomnia or apnea syndrome, alters the normal sleep patterns and the dynamics of the HRV recorded during the night. Objectives: In this paper we analyze long-term and complexity dynamics of the HRV signal recorded during sleep in different groups of subjects. The aim is to investigate whether the calculated indices are able to capture the different characteris tics and to discriminate among the groups of subjects, classified according sleep disorders or cardiovascular pathologies. Methods: Parameters, able to detect the fractal-like behavior of a signal and to measure the regularity and complexity of a time series, are calculated on the HRV signal acquired during the night. Different groups of subjects were analyzed: healthy subjects with high sleep efficiency, healthy subjects with low sleep efficiency, subjects affected by insomnia, heart failure patients, subjects affected by obstructive sleep apnea. Results: The evaluated parameters show significant differences in the groups of subjects considered in this work. In particular heart failure patients have significant lower entropy and complexity values, whereas apnea patients show an increased irregularity when compared with normal subjects with high sleep efficiency. Conclusions: This work proposes indices that can be used as global descriptors of the dynamics of the whole night and can discriminate among different groups of subjects.

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.

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