Introduction to Controversial Topics in Nonlinear Science: Is the Normal Heart Rate Chaotic?

Glass, Leon

doi:10.1063/1.3156832

Cited by 113 publications

(89 citation statements)

References 44 publications

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“…This attempt to interpret variability "directly" in terms of physiological response effectiveness -via SDT below -also differs significantly from some current research approaches: the new field of "chaos theory"-related physiological variability assessment. This rapidly expanding field does indeed provide us with new metrics for variability phenomena (and has affected our analyses) [12]. However, this research area almost never relates the physiological response variability to the subject's requirements for environmental response (i.e.…”

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confidence: 99%

Reduced vagal cardiac control variancein exhausted and high strain job subjects

Collins

Karasek²

2010

International Journal of Occupational Medicine and Environmental Health

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Objectives: This paper has two primary objectives. First, the paper proposes methodological strategies for analyzing multiscale vagal cardiac control based on the Stress Disequilibrium Theory (SDT) using high frequency power of heart rate variability (HFP) and short term variance of HFP. Second, the paper provides evidence of reduced vagal cardiac control range and variability in high strain job and exhausted subjects. Materials and Methods: Job Strain was measured using the Job Content Questionnaire, 8/day diary reports, and a nationally standardized occupational code linkage in 36 healthy midaged males with varying strain jobs. Subjects were Holter-monitored for 48 hours, including a work and rest day. Subjects responded to questions on a daily diary as well as on the Job Content Questionnaire to test for exhaustion as a dichotomous state variable. Vagal cardiac control was measured by components of electrocardiograph: heart rate variability based measures of high frequency power (HFP). We assessed range of vagal cardiac control using extreme value analysis (data in upper tail); and short term vagal variability using Poincaré plots of HFP. Comparisons were made between high (N = 10) and low job strain (N = 22) jobs. Furthermore, subjects categorized as exhausted (N = 4) were separately analyzed. Results: Exhausted subjects displayed a reduced range of vagal cardiac control on the workday; and both high strain and exhausted stubjects displayed reduced short-term variance in vagal cardiac control. A repeated measures ANOVA controlling for age confirms reductions in variance of cardiac vagal activity in high job strain subjects (0.01), with further reductions in subjects reporting exhaustion (p = 0.001). Conclusion: This analysis supports the hypothesis that (a) job strain is associated with reductions in cardiac vagal -or system level -variance; and (b) that reduced system variability may be a characteristic of exhaustion.

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Reduced vagal cardiac control variancein exhausted and high strain job subjects

Collins

Karasek²

2010

International Journal of Occupational Medicine and Environmental Health

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“…In fact, the strongest evidence for chaotic behavior comes from situations in which there is a mathematical model for the dynamics that shows both periodic and chaotic dynamics as the parameters are varied [15]. The existence of chaotic dynamics can be ascertained if this mathematical model fulfils the following criteria: (i) it is deterministic (i.e., does not possess stochastic terms); (ii) its dynamics exhibit bifurcations consistent with the transition to chaos; (iii) embedding of the time series produced by the model shows evidence of a strange attractor; and (iv) it is able to predict experimentally observed chaos [64]. This process is top-down if it starts from strategy (1), or bottom-up if from strategy (2); both processes are possible and should be used.…”

Section: Reconstructing the Underlying Dynamics Of Complex Systemsmentioning

confidence: 99%

“…. non-periodic dynamics in deterministic systems with bounded dynamics and sensitive dependence to initial conditions'' [4,5,64]. In order to be bounded and unstable at the same time, the trajectory of a dissipative dynamical system has been described as a ''strange attractor'' [2,4].…”

Section: Chaos Randomness and (Colored) Noisementioning

confidence: 99%

“…As pointed out by Glass [64], if the above definition of chaos is applied to real systems, the deterministic aspect poses a potentially controversial issue because real systems are not strictly deterministic, the main reason being the existence of noise which is both intrinsic and extrinsic to the system. Noise can represent the extrinsic, unpredictable, influence of the environment, somehow coupled to the system, thus representing an unknown forcing.…”

Section: Chaos Randomness and (Colored) Noisementioning

confidence: 99%

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Chaos in Biochemistry and Physiology

Aon

Cortassa

Lloyd

2011

Encyclopedia of Molecular Cell Biology and Molecular Medicine

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“…However, despite intense research activity in this area, there is limited consensus regarding causation or mechanism and minimal clinical application of the observed phenomena (10). This paper takes a completely different approach, aiming for more fundamental rigor (19)(20)(21)(22)(23)(24) and methods that have the potential for clinical relevance. Here we use and model data from experimental studies of exercising healthy athletes, to add simple physiological explanations for the largest source of HRV and its changes during exercise.…”

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confidence: 99%

Robust efficiency and actuator saturation explain healthy heart rate control and variability

Cruz

Chien

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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The correlation of healthy states with heart rate variability (HRV) using time series analyses is well documented. Whereas these studies note the accepted proximal role of autonomic nervous system balance in HRV patterns, the responsible deeper physiological, clinically relevant mechanisms have not been fully explained. Using mathematical tools from control theory, we combine mechanistic models of basic physiology with experimental exercise data from healthy human subjects to explain causal relationships among states of stress vs. health, HR control, and HRV, and more importantly, the physiologic requirements and constraints underlying these relationships. Nonlinear dynamics play an important explanatory role--most fundamentally in the actuator saturations arising from unavoidable tradeoffs in robust homeostasis and metabolic efficiency. These results are grounded in domain-specific mechanisms, tradeoffs, and constraints, but they also illustrate important, universal properties of complex systems. We show that the study of complex biological phenomena like HRV requires a framework which facilitates inclusion of diverse domain specifics (e.g., due to physiology, evolution, and measurement technology) in addition to general theories of efficiency, robustness, feedback, dynamics, and supporting mathematical tools.system identification | optimal control | respiratory sinus arrhythmia B iological systems display a variety of well-known rhythms in physiological signals (1-6), with particular patterns of variability associated with a healthy state (2-6). Decades of research demonstrate that heart rate (HR) in healthy humans has high variability, and loss of this high HR variability (HRV) is correlated with adverse states such as stress, fatigue, physiologic senescence, or disease (6-13). The dominant approach to analysis of HRV has been to focus on statistics and patterns in HR time series that have been interpreted as fractal, chaotic, scale-free, critical, etc. (6-17). The appeal of time series analysis is understandable as it puts HRV in the context of a broad and popular approach to complex systems (5, 18), all while requiring minimal attention to domain-specific (e.g., physiological) details. However, despite intense research activity in this area, there is limited consensus regarding causation or mechanism and minimal clinical application of the observed phenomena (10). This paper takes a completely different approach, aiming for more fundamental rigor (19)(20)(21)(22)(23)(24) and methods that have the potential for clinical relevance. Here we use and model data from experimental studies of exercising healthy athletes, to add simple physiological explanations for the largest source of HRV and its changes during exercise. We also present methods that can be used to systematically pursue further explanations about HRV that can generalize to less healthy subjects. Fig. 1 shows the type of HR data analyzed, collected from healthy young athletes (n = 5). The data display responses to changes in muscle work rate on a s...

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Introduction to Controversial Topics in Nonlinear Science: Is the Normal Heart Rate Chaotic?

Cited by 113 publications

References 44 publications

Reduced vagal cardiac control variancein exhausted and high strain job subjects

Reduced vagal cardiac control variancein exhausted and high strain job subjects

Chaos in Biochemistry and Physiology

Robust efficiency and actuator saturation explain healthy heart rate control and variability

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