Multiscale entropy analysis of complex heart rate dynamics: discrimination of age and heart failure effects

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.

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Multiscale entropy analysis of heart rate variability in sinoaortic denervated rats

Autonomic Neuroscience ◽

10.1016/j.autneu.2015.07.079 ◽

2015 ◽

Vol 192 ◽

pp. 78

Author(s):

L.E.V. Silva ◽

J.A. Castania ◽

H.C. Salgado ◽

R. Fazan

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Multiscale Entropy ◽

Entropy Analysis

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Multiscale Entropy Analysis of Heart Rate Regularity Changes in Older People with Orthostatic Intolerance

XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016 - IFMBE Proceedings ◽

10.1007/978-3-319-32703-7_13 ◽

2016 ◽

pp. 62-66 ◽

Cited By ~ 2

Author(s):

Marcos Hortelano ◽

Richard B. Reilly ◽

Raquel Cervigón

Keyword(s):

Heart Rate ◽

Older People ◽

Orthostatic Intolerance ◽

Multiscale Entropy ◽

Entropy Analysis

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

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237211002530 ◽

2011 ◽

Vol 23 (04) ◽

pp. 253-260 ◽

Cited By ~ 2

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.

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