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Biomedical subjects

G C Kember

Publications and source records attributed to G C Kember.

18 recordsLinked to original sources

Mechanism of smart baroreception in the aortic arch.

A mechanism is proposed by which the patch of baroreceptors along the inner curvature of the arch of the aorta can sense hemodynamic events occurring downstream from the aortic arch, in the periphery of the arterial tree. Based on a solution of equations governing the elastic movements of the aortic wall, it is shown that the pressure distribution along the patch of baroreceptors has the same functional form as the distribution of strain along the patch. The significance of these findings are discussed, particularly as they relate to the possibility of a neuromechanical basis of essential hypertension.

Animals↗

Stochastic behavior of atrial and ventricular intrinsic cardiac neurons.

To quantify the concurrent transduction capabilities of spatially distributed intrinsic cardiac neurons, the activities generated by atrial vs. ventricular intrinsic cardiac neurons were recorded simultaneously in 12 anesthetized dogs at baseline and during alterations in the cardiac milieu. Few (3%) identified atrial and ventricular neurons (2 of 72 characterized neurons) responded solely to regional mechanical deformation, doing so in a tightly coupled fashion (cross-correlation coefficient r = 0.63). The remaining (97%) atrial and ventricular neurons transduced multimodal stimuli to display stochastic behavior. Specifically, ventricular chemosensory inputs modified these populations such that they generated no short-term coherence among their activities (cross-correlation coefficient r = 0.21 +/- 0.07). Regional ventricular ischemia activated most atrial and ventricular neurons in a noncoupled fashion. Nicotinic activation of atrial neurons enhanced ventricular neuronal activity. Acute decentralization of the intrinsic cardiac nervous system obtunded its neuron responsiveness to cardiac sensory stimuli. Most atrial and ventricular intrinsic cardiac neurons generate concurrent stochastic activity that is predicated primarily upon their cardiac chemotransduction. As a consequence, they display relative independent short-term (beat-to-beat) control over regional cardiac indexes. Over longer time scales, their functional interdependence is manifest as the result of interganglionic interconnections and descending inputs.

Action Potentials↗

SISO extended predictive control-formulation and the basic algorithm.

A new predictive controller is developed that represents a significant change from conventional model predictive control. The method termed extended predictive control (EPC) uses one tuning parameter, the condition number of the system matrix to provide an easy-to-follow tuning procedure. EPC drastically improves the system matrix conditionality resulting in faster closed-loop response without oscillatory transients. The control performance of EPC is compared with the original move suppressed and recently derived shifted predictive controllers, with improved results.

Algorithms↗

Risk aversion predictive control.

A quality-controlled predictive control method, suitable for control of fast, remote systems subject to significant communication delays, is developed. Each move is quality controlled in that it independently satisfies a risk-based control performance criterion. The method is found to be capable of mitigating the ill effects of highly nonstationary delay distributions while providing good control performance for milder nonstationarity. It is demonstrated on simplified predictive control (SPC) of a single-input, single-output process. SPC is preferred here due to its simplicity and well-conditioned dependence of both the sampling time and its single parameter.

Journal Article↗

Continuous analysis of move suppressed and shifted DMC.

"Shifted DMC" (shifted dynamic matrix control) has been empirically shown to have significant improved closed-loop control characteristics over "move-suppressed DMC" where, in the latter, diagonal terms of the dynamic matrix DMC prediction model are augmented to reduce numerical ill conditioning. An added benefit of shifted DMC was that the so-called "shifting parameter," replacing the move suppression parameter, was easily found from the open-loop response. Therefore a novel analytical method, based on a closed form, continuous approximation to closed-loop DMC control, is introduced here and used to quantify the previous empirical results. The dependence of slow and fast time scales of the closed-loop response on the parameters is examined for move-suppressed and shifted DMC methods. It is found that in move-suppressed DMC the slow control time scale is sharply dependent upon the sampling time and move-suppression parameter and that these difficulties are eliminated in shifted DMC.

Journal Article↗

Online optimization of fuzzy-PID control of a thermal process.

A constrained optimization of a simple fuzzy-PID (PID-proportional integral derivative) system is designed for the online improvement of PID control performance during productive control runs. The cost function design yields a desirable balance between rise time, setpoint overshoot, and settling time to the setpoint. The constraints determined by simulation yield control performance no worse than the existing control performance during online optimization. The optimized fuzzy-PID system is compared to a similarly optimized PID controller and a benchmark model predictive controller.

Journal Article↗

On simplified predictive control as a generalization of least-squares dynamic matrix control.

Simplified predictive control (SPC) of a single-input single-output control scheme is compared to the more sophisticated, least-squares formulation of dynamic matrix control (DMC) and its move-suppressed variant (move-suppressed DMC) for a typical two time-step control horizon. A closed-loop, continuous analysis shows that the discrete form of SPC generalizes the discrete DMC algorithm, and its variants, to control responses faster than one-half the process response time while remaining well conditioned.

Journal Article↗

"Smart" baroreception along the aortic arch, with reference to essential hypertension.

Beat-to-beat regulation of heart rate is dependent upon sensing of local stretching or local "disortion" by aortic baroreceptors. Distortions of the aortic wall are due mainly to left ventricular output and to reflected waves arising from the arterial tree. Distortions are generally believed to be useful in cardiac control since stretch receptors or aortic baroreceptors embedded in the adventitia of the aortic wall, transduce the distortions to cardiovascular neural reflex pathways responsible for beat-to-beat regulation of heart rate. Aortic neuroanatomy studies have also found a continuous strip of mechanosensory neurites spread along the aortic inner arch. Although their purpose is now unknown, such a combined sensing capacity would allow measurement of the space and time dependence of inner arch wall distortions due, among other things, to traveling waves associated with pulsatile flow in an elastic tube. We call this sensing capability--"smart baroreception." In this paper we use an arterial tree model to show that the cumulative effects of wave reflections, from many sites far downstream, have a surprisingly pronounced effect on the pressure distribution in the root segment of the tree. By this mechanism global hemodynamics can be focused by wave reflections back to the aortic arch, where they can rapidly impact cardiac control via smart baroreception. Such sensing is likely important to maintain efficient heart function. However, alterations in the arterial tree due to aging and other natural processes can lead in such a system to altered cardiac control and essential hypertension.

Animals↗

Control of cardiac function and noise from a decaying power spectrum.

Evidence is presented that adds to the debate surrounding the question: To what extent does neural control of cardiac output exploit noise? The transduction capability of cardiac afferent neurons, situated in and adjacent to the heart, is vital to feedback in control of cardiac function. An analysis of in situ cardiac afferent activity shows evidence of independent and exponentially distributed interspike intervals. An anatomical basis for such memoryless interspike intervals ultimately derives from the fact that each afferent neuron is associated with a field of sensory neurites, or bare nerve endings, that transduce local chemical and mechanical stimuli in a many-to-one fashion. As such, cardiac afferent neurons and their sensory neurite inputs are respectively modeled here by the Hodgkin-Huxley equations forced by "red" noise (decaying power spectrum) perturbing an otherwise constant subthreshold input. A variable barrier competition model is derived from these equations in order to address the question: How are noisy inputs being processed by sensory neurons to cause each spike? It is found that ion channels are responsible for significant input "whitening" (increased spectral power at higher frequency) through differentiation of the inputs. Such whitening is a means to distinguish low-frequency control signals from otherwise red noise fluctuations. Furthermore, spiking occurs when backward moving averages of the whitened inputs, over a window of the order of the sodium activation time scale, exceed an approximately constant barrier.

Action Potentials↗

Competition model for aperiodic stochastic resonance in a Fitzhugh-Nagumo model of cardiac sensory neurons.

Regional cardiac control depends upon feedback of the status of the heart from afferent neurons responding to chemical and mechanical stimuli as transduced by an array of sensory neurites. Emerging experimental evidence shows that neural control in the heart may be partially exerted using subthreshold inputs that are amplified by noisy mechanical fluctuations. This amplification is known as aperiodic stochastic resonance (ASR). Neural control in the noisy, subthreshold regime is difficult to see since there is a near absence of any correlation between input and the output, the latter being the average firing (spiking) rate of the neuron. This lack of correlation is unresolved by traditional energy models of ASR since these models are unsuitable for identifying "cause and effect" between such inputs and outputs. In this paper, the "competition between averages" model is used to determine what portion of a noisy, subthreshold input is responsible, on average, for the output of sensory neurons as represented by the Fitzhugh-Nagumo equations. A physiologically relevant conclusion of this analysis is that a nearly constant amount of input is responsible for a spike, on average, and this amount is approximately independent of the firing rate. Hence, correlation measures are generally reduced as the firing rate is lowered even though neural control under this model is actually unaffected.

Animals↗

PID gain scheduling using fuzzy logic.

A simple, yet robust and stable alternative to proportional, integral, derivative (PID) gain scheduling is developed using fuzzy logic. This fuzzy gain scheduling allows simple online duplication of PID control and the online improvement of PID control performance. The method is demonstrated with a physical model where PID control performance is improved to levels comparable to model predictive control. The fuzzy formulation is uniquely characterized by; (i) one fuzzy input variable involving the PID manipulated variable, (ii) two parameters to be tuned, while previously tuned PID parameters are retained, and (iii) a gain scheduling differential equation which relates the fuzzy and conventional PID manipulated variables and enables fuzzy gain scheduling.

Journal Article↗

Aperiodic stochastic resonance in a hysteretic population of cardiac neurons.

Aperiodic stochastic resonance (ASR) is studied for a densely interconnected population of excitatory and inhibitory neurons that exhibit hysteresis. Switching between states in the presence of noisy external forcing is represented as a "competition between averages" and this is further explained through a semianalytical model. In contrast to energy-based approaches where only the timing of a switch between states is represented, the competition between averages also identifies the input history responsible for a switch. This last point leads to some interesting conclusions regarding cause and effect in the presence of noisy forcing of a hysteretic system. For example, at subthreshold inputs, it is found that the input history causing a switch between states is primarily dependent upon the noise level even though the corresponding time to switch is sensitive to both the distance from the threshold and the noise level. Since the application considered here is to cardiac neuronal control, control performance is considered over the full input range. Noise tuning for adequate control performance is found to be unnecessary if the noise level is high enough. This is consistent with studies of ASR for sensory neurons. Another observation made here that may be of clinical significance is that at higher noise levels, constraints placed upon inputs to ensure adequate control performance are likely to depend upon the switching direction.

Heart↗

Analytical solutions to a tapering multicylinder somatic shunt cable model for passive neurones.

A multicylinder somatic shunt cable model for passive neurones is considered in which one or more tapering equivalent cylinders emanate from a uniformly polarized soma. Each tapering equivalent cylinder approximates the loss of dendritic trunk parameter in the one or more dendritic trees that it represents, relaxing certain symmetry conditions necessary for the Rall equivalent cylinder concept and in particular allowing terminal branches to end at different electrotonic lengths. The case of exponential taper is considered in detail following the anatomical data on apical and basilar dendrites of CA1 and CA3 hippocampal pyramidal neurons obtained by Turner and Schwartzkroin [J. Neurophysiol. 44 (1980) 184; J. Neurosci. 3 (1983) 2381].

Dendrites↗

KLT-based quality controlled compression of single-lead ECG.

An electrocardiogram (ECG) compression algorithm based on a combination of the Karhunen-Loeve transform (KLT) and multirate sampling is introduced. The use of multirate sampling reduces KLT computational times to those reported for wavelet-packet-based compression techniques. A beat-by-beat quality controlled compression criterion is shown to be necessary to ensure clinically adequate reconstruction of each beat. The resulting quality controlled algorithm efficiently achieves compression rates of approximately 30-40:1 for the MIT-BIH database.

Algorithms↗

Techniques for the application of the analytical solution to the multicylinder somatic shunt cable model for passive neurones.

The general solution for the voltage response to a generic impulse current input in a multicylinder somatic shunt cable model for passive neurones has been developed in [1]. In this paper we consider the application of the multicylinder solution to examples previously considered by other authors for the single cylinder case: long and short current input and synaptic input modeled by an alpha-function and a multi-exponential function. Simple expansions appropriate for small and large times are found and efficient means of obtaining these expansions are clearly demonstrated. The dependence of the small and large time solutions upon the dimensionless parameters appearing in the conservation of current condition at the soma is investigated. Relevant limits of these dimensionless parameters which further simplify the small and large time solutions are related back to equivalent dimensional problems of interest to the practitioner. The well-posedness of the dimensionless inverse problem is investigated and a method proposed for the solution of the dimensional inverse problem for the somatic shunt.

Animals↗

Analytical solutions to the multicylinder somatic shunt cable model for passive neurones with differing dendritic electrical parameters.

The multicylinder somatic shunt cable model for passive neurones with differing time constants in each cylinder is considered in this paper. The solution to the model with general inputs is developed, and the parameteric dependence of the voltage response is investigated. The method of analysis is straightforward and follows that laid out in Evans et al. (1992, 1994): (i) The dimensional problem is stated with general boundary and initial conditions. (ii) The model is fully non-dimensionalised, and a dimensionless parameter family which uniquely governs the behaviour of the dimensionless voltage response is obtained. (iii) The fundamental unit impulse problem is solved, and the solutions to problems involving general inputs are written in terms of the unit impulse solution. (iv) The large and small time behaviour of the unit impulse solution is examined. (v) The parametric dependence of the unit impulse upon the dimensionless parameter family is explored for two limits of practical interest. A simple expression for the principle relationship between the dimensionless parameter family is derived and provides insight into the interaction between soma and cylinders. A well-posed method for the solution of the dimensional inverse problem is presented.

Adult↗

Techniques for obtaining analytical solutions to the multicylinder somatic shunt cable model for passive neurones.

The somatic shunt cable model for neurones is extended to the case in which several equivalent cylinders, not necessarily of the same electrotonic length, emanate from the cell soma. The cable equation is assumed to hold in each cylinder and is solved with sealed end conditions and a lumped soma boundary condition at a common origin. A Green's function (G) is defined, corresponding to the voltage response to an instantaneous current pulse at an arbitrary point along one of the cylinders. An eigenfunction expansion for G is obtained where the coefficients are determined using the calculus of residues and compared with an alternative method of derivation using a modified orthogonality condition. This expansion converges quickly for large time, but, for small time, a more convenient alternative expansion is obtained by Laplace transforms. The voltage response to arbitrary currents injected at arbitrary sites in the dendritic tree (including the soma) may then be expressed as a convolution integral involving G. Illustrative examples are presented for a point charge input.

Animals↗