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Biomedical subjects

J V Ringwood

Publications and source records attributed to J V Ringwood.

5 recordsLinked to original sources
PubMed2006

Sampling period determination for heart rate logging under an exercise regimen.

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 work-rate/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.

Adult↗
PubMed2001

Slow oscillations in blood pressure via a nonlinear feedback model.

Blood pressure is well established to contain a potential oscillation between 0.1 and 0.4 Hz, which is proposed to reflect resonant feedback in the baroreflex loop. A linear feedback model, comprising delay and lag terms for the vasculature, and a linear proportional derivative controller have been proposed to account for the 0.4-Hz oscillation in blood pressure in rats. However, although this model can produce oscillations at the required frequency, some strict relationships between the controller and vasculature parameters must be true for the oscillations to be stable. We developed a nonlinear model, containing an amplitude-limiting nonlinearity that allows for similar oscillations under a very mild set of assumptions. Models constructed from arterial pressure and sympathetic nerve activity recordings obtained from conscious rabbits under resting conditions suggest that the nonlinearity in the feedback loop is not contained within the vasculature, but rather is confined to the central nervous system. The advantage of the model is that it provides for sustained stable oscillations under a wide variety of situations even where gain at various points along the feedback loop may be altered, a situation that is not possible with a linear feedback model. Our model shows how variations in some of the nonlinearity characteristics can account for growth or decay in the oscillations and situations where the oscillations can disappear altogether. Such variations are shown to accord well with observed experimental data. Additionally, using a nonlinear feedback model, it is straightforward to show that the variation in frequency of the oscillations in blood pressure in rats (0.4 Hz), rabbits (0.3 Hz), and humans (0.1 Hz) is primarily due to scaling effects of conduction times between species.

Animals↗
PubMed2001

Dynamic relationship between sympathetic nerve activity and renal blood flow: a frequency domain approach.

Blood pressure displays an oscillation at 0.1 Hz in humans that is well established to be due to oscillations in sympathetic nerve activity (SNA). However, the mechanisms that control the strength or frequency of this oscillation are poorly understood. The aim of the present study was to define the dynamic relationship between SNA and the vasculature. The sympathetic nerves to the kidney were electrically stimulated in six pentobarbital-sodium anesthetized rabbits, and the renal blood flow response was recorded. A pseudo-random binary sequence (PRBS) was applied to the renal nerves, which contains equal spectral power at frequencies in the range of interest (<1 Hz). Transfer function analysis revealed a complex system composed of low-pass filter characteristics but also with regions of constant gain. A model was developed that accounted for this relationship composed of a 2 zero/4 pole transfer function. Although the position of the poles and zeros varied among animals, the model structure was consistent. We also found the time delay between the stimulus and the RBF responses to be consistent among animals (mean 672 +/- 22 ms). We propose that the identification of the precise relationship between SNA and renal blood flow (RBF) is a fundamental and necessary step toward understanding the interaction between SNA and other physiological mediators of RBF.

Animals↗
PubMed1999

Anaerobic threshold measurement using dynamic neural network models.

This paper deals with the subject of anaerobic threshold measurements for athletes involved in aerobic or aerobic/anaerobic sports. Traditionally, anaerobic threshold has been determined using invasive tests or using a non-invasive technique using steady-state heart-rate/work rate data. Non-invasive tests have the advantage of not requiring specialised equipment, but the acquisition of steady-state information can be problematic. This paper demonstrates how dynamical data can be used to accurately determine the steady-state heart-rate/work-rate curve (SSHW curve) using neural network dynamic models.

Anaerobic Threshold↗