Interactions between short-term and long-term cardiovascular control mechanisms

Hoyer, Dirk; Frank, Birgit; Götze, Christine; Stein, Phyllis K.; Żebrowski, Jan J.; Baranowski, Rafał; Palacios, M.; Vallverdú, Montserrat; Caminal, P.; Luna, Anthony Bayés de; Schmidt, Georg; Schmidt, Hendrik

doi:10.1063/1.2710448

Cited by 21 publications

(15 citation statements)

References 21 publications

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“…Recently introduced nonlinear measures, such as power law slope, 10,11 detrended fluctuation analysis (DFA), 12,13 symbolic dynamics, and empirical mode decomposition indices, 14 indicate the relevance of complex interrelationships. The concept of a fundamental inverse relationship between short‐term and long‐tem cardiovascular control was found in clinical data and confirmed by mathematical modeling 15 …”

Section: Introductionmentioning

confidence: 71%

“…In patients at higher risk, the beat‐to‐beat complexity was increased, but at the same time, the long‐term complexity was decreased, suggesting that AIF‐based measures reflect a readjustment between a short‐term dysfunction due to CHF and a long‐term cardiovascular compensating response aimed at achieving a new homeostatic equilibrium in cardiovascular performance under pathophysiological conditions. The computation of a composite risk index taking into account the inverse relationship between short‐ and long‐term AIF 15 further improved the predictive value of the information flow characteristics.…”

Section: Discussionmentioning

confidence: 99%

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Autonomic Response to Cardiac Dysfunction in Chronic Heart Failure: A Risk Predictor Based on Autonomic Information Flow

Hoyer

Maestri

Rovere

et al. 2008

Pacing Clinical Electrophis

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Section: Introductionmentioning

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Autonomic Response to Cardiac Dysfunction in Chronic Heart Failure: A Risk Predictor Based on Autonomic Information Flow

Hoyer

Maestri

Rovere

et al. 2008

Pacing Clinical Electrophis

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“…For example, attention-deficit hyperactivity disorders in adolescent boys were related to antenatal maternal anxiety (46) that potentially influences the development of fetal humoral and autonomic control reflected in heart rate patterns. Reorganization of autonomic control due to several systemic pathologies was associated with a systematic readjustment of complexity from different time scales in adults (12,15) and may indicate the importance of considering scale-depending adjustments in the prenatal diagnosis too.…”

Section: R386 Development Of Complex Autonomic Control In the Fetusmentioning

confidence: 99%

“…Power widely spread over all frequencies indicates high irregularity in contrast to distinct peaks that indicate predominant rhythms and, hence, regular patterns. The physiological interpretation of multiscale complexity is less clear since regulating influences in a complex dynamic system can affect all scales (5,12,15,17). Scales of low complexity indicate regular patterns with respective periodicity.…”

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confidence: 99%

Fetal development of complex autonomic control evaluated from multiscale heart rate patterns

Hoyer

Nowack

Bauer

et al. 2013

American Journal of Physiology-Regulatory, Integrative and Comp

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U. Fetal development of complex autonomic control evaluated from multiscale heart rate patterns. Am J Physiol Regul Integr Comp Physiol 304: R383-R392, 2013. First published December 26, 2012 doi:10.1152/ajpregu.00120.2012.-Development of the fetal autonomic nervous system's integrative capacity in relation to gestational age and emerging behavioral pattern is reflected in fetal heart rate patterns. Conventional indices of vagal and sympathetic rhythms cannot sufficiently reflect their complex interrelationship. Universal behavioral indices of developing complex systems may provide additional information regarding the maturating complex autonomic control. We investigated fetal magnetocardiographic recordings undertaken at 10-min intervals in active (n ϭ 248) and quiet (n ϭ 111) states between 22 and 39 wk gestational age. Standard deviation of heartbeat intervals, skewness, contribution of particular rhythms to the total power, and multiscale entropy were analyzed. The multiscale entropy methodology was validated for 10-min data sets. Age dependence was analyzed by linear regression. In the quiet state, contribution of sympathovagal rhythms and their complexity over a range of corresponding short scales increased with rising age, and skewness shifted from negative to positive values. In the active state, age dependencies were weaker. Skewness as the strongest parameter shifted in the same direction. Fluctuation amplitude and the complexity of scales associated with sympathovagal rhythms increased. We conclude that in the quiet state, stable complex organized rhythms develop. In the active state, however, increasing behavioral variability due to multiple internal coordinations, such as movement-related heart rate accelerations, and external influences develop. Hence, the state-selective assessment in association with developmental indices used herein may substantially improve evaluation of maturation age and early detection and interpretation of developmental problems in prenatal diagnosis. autonomic nervous system; complexity; developmental biology; fetal maturation; fetal behavioral patterns THERE IS INCREASING CONSENSUS that a certain number of diseases such as cardiovascular disease, metabolic syndrome, atherosclerosis, Type 2 diabetes, learning difficulties, hyperkinetic disorders, as well as cognitive, behavioral, and emotional problems in late postnatal age are associated with adverse influences during fetal development that became permanently programmed (1,2,4,10,11,19,20,23,24,26,31,32,39,40,44,47). However, early identification of fetal developmental problems is a challenging topic of prenatal diagnosis due to the extremely limited number of observable variables from the fetus. During the fetal maturation the emerging complex behavioral patterns are associated with an increasing functional integration in the organism. In that connection the autonomic nervous system (ANS) plays a predominant role for complex coordinated control of multiple vitally important physiological subsystems in the organism. Sin...

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“…This systematic check requires the association of a multiscale analysis tool exploiting a nonlinear discriminating statistic (5,6,55) with a test capable to detect nonlinear dynamics (42,51). Until now, this association has been rarely exploited in conditions of impaired cardiac control (16,47) and is mostly limited to traditional monoscale nonlinearity test (28,32,46), or multiscale analysis has been applied without testing the presence of nonlinearities (6,14,43,44,55).…”

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confidence: 99%

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

Silva

Lataro

Castania

et al. 2017

Journal of Applied Physiology

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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. 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.

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Interactions between short-term and long-term cardiovascular control mechanisms

Cited by 21 publications

References 21 publications

Autonomic Response to Cardiac Dysfunction in Chronic Heart Failure: A Risk Predictor Based on Autonomic Information Flow

Autonomic Response to Cardiac Dysfunction in Chronic Heart Failure: A Risk Predictor Based on Autonomic Information Flow

Fetal development of complex autonomic control evaluated from multiscale heart rate patterns

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

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