Luiz Eduardo Virgilio Silva

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

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.

The role of sympathetic and vagal cardiac control on complexity of heart rate dynamics

Analysis of heart rate variability (HRV) by nonlinear approaches has been gaining interest due to their ability to extract additional information from heart rate (HR) dynamics that are not detectable by traditional approaches. Nevertheless, the physiological interpretation of nonlinear approaches remains unclear. Therefore, we propose long-term (60 min) protocols involving selective blockade of cardiac autonomic receptors to investigate the contribution of sympathetic and parasympathetic function upon nonlinear dynamics of HRV. Conscious male Wistar rats had their electrocardiogram (ECG) recorded under three distinct conditions: basal, selective (atenolol or atropine), or combined (atenolol plus atropine) pharmacological blockade of autonomic muscarinic or β1-adrenergic receptors. Time series of RR interval were assessed by multiscale entropy (MSE) and detrended fluctuation analysis (DFA). Entropy over short (1 to 5, MSE1-5) and long (6 to 30, MSE6-30) time scales was computed, as well as DFA scaling exponents at short (αshort, 5 ≤ n ≤ 15), mid (αmid, 30 ≤ n ≤ 200), and long (αlong, 200 ≤ n ≤ 1,700) window sizes. The results show that MSE1-5 is reduced under atropine blockade and MSE6-30 is reduced under atropine, atenolol, or combined blockade. In addition, while atropine expressed its maximal effect at scale six, the effect of atenolol on MSE increased with scale. For DFA, αshort decreased during atenolol blockade, while the αmid increased under atropine blockade. Double blockade decreased αshort and increased αlong Results with surrogate data show that the dynamics during combined blockade is not random. In summary, sympathetic and vagal control differently affect entropy (MSE) and fractal properties (DFA) of HRV. These findings are important to guide future studies.NEW & NOTEWORTHY Although multiscale entropy (MSE) and detrended fluctuation analysis (DFA) are recognizably useful prognostic/diagnostic methods, their physiological interpretation remains unclear. The present study clarifies the effect of the cardiac autonomic control on MSE and DFA, assessed during long periods (1 h). These findings are important to help the interpretation of future studies.

Toll-like receptor 9 plays a key role in the autonomic cardiac and baroreflex control of arterial pressure

The crosstalk between the immune and the autonomic nervous system may impact the cardiovascular function. Toll-like receptors are components of the innate immune system and play developmental and physiological roles. Toll-like receptor 9 (TLR9) is involved in the pathogenesis of cardiovascular diseases, such as hypertension and heart failure. Since such diseases are commonly accompanied by autonomic imbalance and lower baroreflex sensitivity, we hypothesized that TLR9 modulates cardiac autonomic and baroreflex control of arterial pressure (AP). Toll-like receptor 9 knockout (TLR9 KO) and wild-type (WT) mice were implanted with catheters into carotid artery and jugular vein and allowed to recover for 3 days. After basal recording of AP, mice received methyl-atropine or propranolol. AP and pulse interval (PI) variability were evaluated in the time and frequency domain (spectral analysis), as well as by multiscale entropy. Spontaneous baroreflex was studied by sequence technique. Behavioral and cardiovascular responses to fear-conditioning stress were also evaluated. AP was similar between groups, but TLR9 KO mice exhibited lower basal heart rate (HR). AP variability was not different, but PI variability was increased in TLR9 KO mice. The total entropy was higher in TLR9 KO mice. Moreover, baroreflex function was found higher in TLR9 KO mice. Atropine-induced tachycardia was increased in TLR9 KO mice, whereas the propranolol-induced bradycardia was similar to WT mice. TLR9 KO mice exhibit increased behavioral and decreased tachycardia responses to fear-conditioning stress. In conclusion, our findings suggest that TLR9 may negatively modulate cardiac vagal tone and baroreflex in mice.

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

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.

Two-dimensional sample entropy: assessing image texture through irregularity

Image texture analysis is a key task in computer vision. Although various methods have been applied to extract texture information, none of them are based on the principles of sample entropy, which is a measurement of entropy rate. This paper proposes a two-dimensional sample entropy method, namely SampEn2D, in order to measure irregularity in pixel patterns. We evaluated the proposed method in three different situations: a set of simulated images generated by a deterministic function corrupted with different levels of a stochastic influence; the Brodatz public texture database; and a real biological image set of rat sural nerve. Evaluation with simulations showed SampEn2D as a robust irregularity measure, closely following sample entropy properties. Results with Brodatz dataset testified superiority of SampEn2D to separate different image categories compared to conventional Haralick and wavelet descriptors. SampEn2D was also capable of discriminating rat sural nerve images by age groups with high accuracy (AUROC = 0.844). No significant difference was found between SampEn2D AUROC and those obtained with the best performed Haralick descriptors, i.e. entropy (AUROC = 0.828), uniformity (AUROC = 0.833), homogeneity (AUROC = 0.938) and Wavelet descriptors, i.e. Haar energy/entropy (AUROC = 0.932) and Daubechies energy/entropy (AUROC = 0.859). In addition, it was shown that SampEn2D computation time increases with image size, being around 1400 s for a 600 × 600 pixels image. In conclusion, SampEn2D showed to be stable and robust enough to be applied as texture feature quantifier and irregularity properties, as measured by SampEn2D, seem to be an important feature for image characterization in biomedical image analysis.

Mesenchymal Stem Cells Improve Heart Rate Variability and Baroreflex Sensitivity in Rats with Chronic Heart Failure

Heart failure induced by myocardial infarct (MI) attenuates the heart rate variability (HRV) and baroreflex sensitivity, which are important risk factors for life-threatening cardiovascular events. Therapies with mesenchymal stem cells (MSCs) have shown promising results after MI. However, the effects of MSCs on hemodynamic (heart rate and arterial pressure) variability and baroreflex sensitivity in chronic heart failure (CHF) following MI have not been evaluated thus far. Male Wistar rats received MSCs or saline solution intravenously 1 week after ligation of the left coronary artery. Control (noninfarcted) rats were also evaluated. MI size was assessed using single-photon emission computed tomography (SPECT). The left ventricular ejection fraction (LVEF) was evaluated using radionuclide ventriculography. Four weeks after MSC injection, the animals were anesthetized and instrumented for chronic ECG recording and catheters were implanted in the femoral artery to record arterial pressure. Arterial pressure and HRVs were determined in time and frequency domain (spectral analysis) while HRV was also examined using nonlinear methods: DFA (detrended fluctuation analysis) and sample entropy. The initial MI size was the same among all infarcted rats but was reduced by MSCs. CHF rats exhibited increased myocardial interstitial collagen and sample entropy combined with the attenuation of the following cardiocirculatory parameters: DFA indices, LVEF, baroreflex sensitivity, and HRV. Nevertheless, MSCs hampered all these alterations, except the LVEF reduction. Therefore, 4 weeks after MSC therapy was applied to CHF rats, MI size and myocardial interstitial fibrosis decreased, while baroreflex sensitivity and HRV improved.

Changes in the Complexity of Heart Rate Variability with Exercise Training Measured by Multiscale Entropy-Based Measurements

Quantifying complexity from heart rate variability (HRV) series is a challenging task, and multiscale entropy (MSE), along with its variants, has been demonstrated to be one of the most robust approaches to achieve this goal. Although physical training is known to be beneficial, there is little information about the long-term complexity changes induced by the physical conditioning. The present study aimed to quantify the changes in physiological complexity elicited by physical training through multiscale entropy-based complexity measurements. Rats were subject to a protocol of medium intensity training (n=13) or a sedentary protocol (n=12). One-hour HRV series were obtained from all conscious rats five days after the experimental protocol. We estimated MSE, multiscale dispersion entropy (MDE) and multiscale SDiffq from HRV series. Multiscale SDiffq is a recent approach that accounts for entropy differences between a given time series and its shuffled dynamics. From SDiffq, three attributes (q-attributes) were derived, namely SDiffqmax, qmax and qzero. MSE, MDE and multiscale q-attributes presented similar profiles, except for SDiffqmax. qmax showed significant differences between trained and sedentary groups on Time Scales 6 to 20. Results suggest that physical training increases the system complexity and that multiscale q-attributes provide valuable information about the physiological complexity.

Baroreflex control of renal sympathetic nerve activity in early heart failure assessed by the sequence method

The integrity of the baroreflex control of sympathetic activity in heart failure (HF) remains under debate. We proposed the use of the sequence method to assess the baroreflex control of renal sympathetic nerve activity (RSNA). The sequence method assesses the spontaneous arterial pressure (AP) fluctuations and their related changes in heart rate (or other efferent responses), providing the sensitivity and the effectiveness of the baroreflex. Effectiveness refers to the fraction of spontaneous AP changes that elicits baroreflex‐mediated variations in the efferent response. Using three different approaches, we showed that the baroreflex sensitivity between AP and RSNA is not altered in early HF rats. However, the sequence method provided evidence that the effectiveness of baroreflex in changing RSNA in response to AP changes is markedly decreased in HF. The results help us better understand the baroreflex control of the sympathetic nerve activity.

Two-dimensional sample entropy analysis of rat sural nerve aging.

Entropy analysis of images are usually performed using Shannon entropy, which calculates the probability of occurrency of each gray level on the image. However, not only the pixel gray level but also the spatial distribution of pixels might be important for image analysis. On the other hand, sample entropy (SampEn) is an important tool for estimation of irregularity in time series, which calculates the probability of pattern occurrence within the series. Therefore, we propose here an extension of SampEn to a two-dimensional case, namely SampEn2D, as an entropy method for extracting features from images that accounts for the spatial distribution of pixels. SampEn2D was applied to histological segments of sural nerve obtained from young (30 days) and elderly (720 days) rats. Morphometric indexes, such as the total number of myelinated fibers and the average myelinated fibers area and perimeter were also calculated. Results show that SampEn2D can extract useful information from histological nerve images, classifying elderly rat image as more regular than young rat. As SampEn2D is related to irregularity/unpredictability, we can conclude that the proposed method is complementary to morphometric indexes. Further studies are being built to validate SampEn2D.

Comparison between spectral analysis and symbolic dynamics for heart rate variability analysis in the rat

Spectral analysis of heart rate (HR) has been widely used to assess the autonomic cardiovascular control. A nonlinear approach, known as symbolic analysis, has been reported to be very useful to assess the autonomic control of cardiovascular system in humans, but very few studies reported on the differences between these two approaches on experimental models. Two distinct approaches were used to elicit autonomic changes in conscious Wistar rats: (1) pharmacological blockade of cardiac autonomic receptors with atenolol (ATE, N = 9) or methylatropine (ATR, N = 9) and (2) mild changes in arterial pressure (AP) induced by phenylephrine (PHE, N = 9) or sodium nitroprusside (NPS, N = 9). Series of cardiac interval (CI) and systolic AP (SAP) were assessed using spectral analysis and symbolic dynamics. Results show that, for spectral analysis, the power in high frequency band of CI and the power in low frequency band of SAP are the most reliable indices of vagal and sympathetic modulation, respectively. For symbolic analysis, results point 0V% and 1V% to be related to sympathetic and 2UV% to vagal modulation. Interestingly, the incidence of 1V patterns, hitherto with unknown meaning, was revealed the best index of sympathetic modulation in the rat and should be accounted for in the future studies.