Dynamics of ventilation and heart rate in response to sinusoidal work load in man

Wigertz, Ove

doi:10.1152/jappl.1970.29.2.208

Cited by 68 publications

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“…In the last decade, several physiologists have used sinusoidal forcings to explore the dynamics of cardiorespiratory responses to muscular exercise in man. WIGERTZ (1970) investigated the dynamic characteristics of heart rate and ventilation. CASABURI et al (1977) and BAKKER et al (1980) compared the kinetics of heart rate, ventilation, and gas exchange parameters to define the interrelationship among variables during exercise.…”

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Cardiorespiratory dynamics during sinusoidal and impulse exercise in man.

Miyamoto

Nakazono

et al. 1983

Jpn.J.Physiol

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Dynamic characteristics of ventilation, cardiac output, and gas exchange during sinusoidally varying work rates for the periods from 1 to 12 min and impulse work rate with a duration of 10 sec were studied on five healthy men in an upright position. Changes in work rate were given by controlling externally the electromagnetic braking system of a bicycle ergometer. Stroke volume, heart rate, and cardiac output during exercise were determined continuously by using an automated impedance cardiograph. Breath by breath determination in minute ventilation, respiratory frequency, tidal volume, oxygen consumption, carbon dioxide output, end-tidal pressures of oxygen and carbon dioxide, and gas exchange ratio were conducted. From these and steady-state response data amplitude and phase relations between each variable and the input work loads were obtained utilizing the frequency analysis techniques. The response characteristics to sinusoidal stimuli were well represented by first-order models with time constants for VE, Vcoz, V o ,, and Q averaging 75, 67, 52, and 36 sec, respectively. The kinetics of HR closely resembled that of Q. There was a close link between both the dynamics of VE and Vco 2. On the other hand, the responses to impulse stimuli were better described by second-order models in which fast and slow response components were connected in parallel. However, the contribution of the fast component to total response was small. Although this response may support in its form the neurohumoral concept to explain exercise hyperpnea, a tight linkage was observed between VE and VCO2 responses to impulse stimuli. Thus, hyperpnea during the unsteady-state of exercise may be explained by the cardiodynamic hypothesis.

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confidence: 99%

Cardiorespiratory dynamics during sinusoidal and impulse exercise in man.

Miyamoto

Nakazono

et al. 1983

Jpn.J.Physiol

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“…In addition to mechanistic physiological models, we also use systems identification techniques (referred to as "black-box" fits in this paper) (25,33,36,37) as intermediate steps to identify parsimonious canonical dynamical input-output models relating HR as an output variable to input disturbances such as workload and ventilation. These techniques establish causal deterministic links between input and output variables, highlight the aspects of time series and dynamic relationships that are explored further, and give some indication of the degree of complexity of their dynamics.…”

Section: Physiological Tradeoffsmentioning

confidence: 99%

“…Results from all subjects showed qualitatively similar nonlinearities (SI Appendix). We will argue that this saturating nonlinearity is the simplest and most fundamental example of change in HRV in response to stressors (11,12,25) [exercise in the experimental case, but in general also fatigue, dehydration, trauma, infection, even fear and anxiety (6-9, 11, 12, 25)].Physiologists have correlated HRV and autonomic tone (7,11,12,14), and the (im)balance between sympathetic stimulation and parasympathetic withdrawal (12,(26)(27)(28). The alternation in autonomic control of HR (more sympathetic and less parasympathetic tone during exercise) serves as an obvious proximate cause for how the HRV changes as shown in Fig.…”

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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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“…For example, Yamamoto et al (1988) have looked at using a pseudo random binary sequence (PRBS)-like excitation signal for work rate but have not approached ''anaerobic threshold'' at all during the exercise protocol. Wigertz (1970) and Bakker, Struikenkamp and De Vries (1980) used sinusoid excitation of the work-rate/heart-rate system to model the ventilation and heart rate response to exercise. However, neither study stresses the entire work-rate range, relying instead on a small portion of the potential exercise range and thus leading to an incomplete system description.…”

Section: Introductionmentioning

confidence: 99%

Sampling period determination for heart rate logging under an exercise regimen

McCarthy¹,

Ringwood

2006

Journal of Sports Sciences

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Using a mathematical procedure, we determine appropriate sampling rates for logging heart rate, at a variety of exercise intensities. The mathematical procedure involves correlating exercise and heart rate data to determine a dynamical mathematical model, from which the frequency response of the relationship between exercise intensity and heart rate can be determined. The sampling rate is then straightforwardly deduced by making appropriate measurements on the frequency response curve. We show how careful consideration needs to be given to the choice of dynamical model structure and the work regimen, so that consistent and convincing conclusions can be drawn. We demonstrate that the dynamics of the workrate/heart-rate system are dependent on the nominal work/heart rate, but a 5-s sampling period, as used in many commercial heart rate monitors, appears to be adequate, especially when some averaging is performed before logging.

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Dynamics of ventilation and heart rate in response to sinusoidal work load in man

Cited by 68 publications

References 0 publications

Cardiorespiratory dynamics during sinusoidal and impulse exercise in man.

Cardiorespiratory dynamics during sinusoidal and impulse exercise in man.

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

Sampling period determination for heart rate logging under an exercise regimen

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