Studying Cardiac Neural Network Dynamics: Challenges and Opportunities for Scientific Computing (original) (raw)
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IEEE Transactions on Biomedical Engineering, 1988
This paper deals with parametric methods for processing cardiovascular signals in order to contribute to a new developing approach and it tries to provide a global, although indirect, evaluation of some neural regulatory activities. In particular, the variability signals of heart rate (under the form of interval tachogram) and arterial blood pressure (systogram) together with respiratory movement signal (respirogram) are considered as inputs to a closed-loop model which describes a few aspects of the physiological interactions among the signals themselves. The identifiability of the transfer function of the model is demonstrated from the joint process black box description of the signals. A direct identification procedure is proposed dividing the system into two dynamic adjustment models: the generalized least-squares algorithm is applied. Through proper simulations and applications on real data some considerations a r e made about the causal relations existing among the variability signals. A few suggestions are deduced on how and where the respirogram enters the model and on the genesis of the 10 s rhythm, besides obtaining parameters relevant to Starling effect, to Windkessel model, and to the gain of baroreceptor mechanisms. The approach presented is intended also to provide a general frame for closed-loop identification in different pathophysiological conditions.
Inferring cardiovascular control from spontaneous variability
Autonomic Neuroscience, 2013
When observed on a beat-to-beat basis cardiovascular variables exhibit spontaneous rhythmical fluctuations about their mean values even in the absence of any external stimulus and under well-controlled experimental conditions (e.g. in resting supine state in quiet environment). A constantly growing amount of literature suggests that these beat-to-beat variations are the result of reflex and non-reflex (i.e. autonomous) control mechanisms operating over different time scales aiming at the maintenance of cardiovascular variables within safe intervals of values while guaranteeing a rapid adaptation to fulfill the variable demands of the organism. One of the peculiar features of cardiovascular variabilities is its partial predictability suggesting an underlying determinism and prompting for the search of rules governing these variations and the cardiovascular control underpinning them. Since the early recognition of the importance of the analysis of cardiovascular variabilities , it has appeared clear that inferring cardiovascular regulatory mechanisms from spontaneous variability would have been a complex task requiring the contemporaneous application of several techniques . The complexity of the task raises from the multiplicity of mechanisms contemporaneously active to maintain homeostasis, the vague nature of the relationships among cardiovascular variables, the variable strength of the interactions, the relatively large amount of temporal scales involved in short-term regulation even when restricting the analysis over recordings of few minutes, the presence of nonlinearities capable of generating completely different behaviors in response to the same input in the presence of small changes of system parameters, the distributed nature of some mechanisms (e.g. vasomotion) leading to independent or weakly dependent activities that can be easily synchronized or entrained by common triggering inputs (e.g. sympathetic drive), and the incidence of non-stationarities resulting from the variety of interacting subsystems capable of imposing its own specific dynamics when one component takes priority over the others.
Towards predictive modelling of the electrophysiology of the heart
Experimental Physiology, 2008
The simulation of cardiac electrical function is an example of a successful integrative multiscale modelling approach that is directly relevant to human disease. Today we stand at the threshold of a new era, in which anatomically detailed, tomographically reconstructed models are being developed that integrate from the ion channel to the electromechanical interactions in the intact heart. Such models hold high promise for interpretation of clinical and physiological measurements, for improving the basic understanding of the mechanisms of dysfunction in disease, such as arrhythmias, myocardial ischaemia and heart failure, and for the development and performance optimization of medical devices. The goal of this article is to present an overview of current state-of-art advances towards predictive computational modelling of the heart as developed recently by the authors of this article. We first outline the methodology for constructing electrophysiological models of the heart. We then provide three examples that demonstrate the use of these models, focusing specifically on the mechanisms for arrhythmogenesis and defibrillation in the heart. These include: (1) uncovering the role of ventricular structure in defibrillation; (2) examining the contribution of Purkinje fibres to the failure of the shock; and (3) using magnetic resonance imaging reconstructed heart models to investigate the re-entrant circuits formed in the presence of an infarct scar.
Fausto Lucena, D.S. Brito, Allan Kardec Barros, and Noboru Ohnishi, 2008
Herein, we make a theoretical effort to characterize the interplay of the main stimuli underlying the cardiac control. Based on the analysis of heartbeat intervals and using neural coding strategies, we investigate the hypothesis that information theoretic principles could be used to give insights to the strategy evolved to control the heart. This encodes the sympathetic and parasympathetic stimuli. As a result of analysis, we illustrate and emphasize the basic sources that might be attributed to control the heart rate based on the interplay of the autonomic tones.
Modelling the heart as a communication system
Journal of the Royal Society, Interface / the Royal Society, 2015
Electrical communication between cardiomyocytes can be perturbed during arrhythmia, but these perturbations are not captured by conventional electrocardiographic metrics. We developed a theoretical framework to quantify electrical communication using information theory metrics in two-dimensional cell lattice models of cardiac excitation propagation. The time series generated by each cell was coarse-grained to 1 when excited or 0 when resting. The Shannon entropy for each cell was calculated from the time series during four clinically important heart rhythms: normal heartbeat, anatomical reentry, spiral reentry and multiple reentry. We also used mutual information to perform spatial profiling of communication during these cardiac arrhythmias. We found that information sharing between cells was spatially heterogeneous. In addition, cardiac arrhythmia significantly impacted information sharing within the heart. Entropy localized the path of the drifting core of spiral reentry, which co...
A model of neural control of the heart rate
Physica A: Statistical Mechanics and its Applications, 1995
A simple model of heart rate regulation is proposed, using the assumption that the nervous system regulates the generation of pulses of the pacemaker. Previous values of intervals between heart beats (RR intervals) are used for this regulation, which is described by a nonlinear feedback loop with time delay. The conductance of the excitation in the heart is phenomenologically described by one nonlinear function (recovery curve). The model reproduces time series of RR intervals. Different known patterns of heart rate variability are observed, depending on the type of control and the parameter values.
Up to Date Issues on Modeling the Nervous Control of the Cardiovascular System on Short-Term
Proceedings of the 18th IFAC World Congress, 2011
Physiological control mechanisms are receiving an increasing amount of attention due to their primary role in homeostasis, while systems theory is becoming an indispensable tool for understanding the dynamic behaviors that emerge. In this context, a topic that has been intensively studied is the nervous control of the cardiovascular system on short term. This paper tries to develop a common framework for future efforts in modeling the nervous control of the cardiovascular system in orthostatic stress, through a systemic analyze of the roles and interactions of the regulatory mechanisms identified in the literature.
Behavioral-Independent Features of Complex Heartbeat Dynamics
We test whether the complexity of the cardiac interbeat interval time series is simply a consequence of the wide range of scales characterizing human behavior, especially physical activity, by analyzing data taken from healthy adult subjects under three conditions with controls: (i) a "constant routine" protocol where physical activity and postural changes are kept to a minimum, (ii) sympathetic blockade, and (iii) parasympathetic blockade. We find that when fluctuations in physical activity and other behavioral modifiers are minimized, a remarkable level of complexity of heartbeat dynamics remains, while for neuroautonomic blockade the multifractal complexity decreases.
Multiscale Aspects of Cardiac Control
We report some recent attempts to understand the dynamics of complex physiologic fluctuations by adapting and extending concepts and methods developed very recently in statistical physics. We first review recent progress using wavelet-based multifractal analysis, magnitude and sign decomposition analysis and a new segmentation algorithm to quantify multiscale features of heartbeat interval series. We then investigate how heartbeat dynamics change with circadian influences and under pathologic conditions, and we discuss their possible relation to the underlaying cardiac control mechanisms. The analytic tools we discuss may be used on a wider range of physiologic signals. r (P.Ch. Ivanov). biology. In particular, physiologic systems under autonomic regulation, such as heart rate, are good candidates for a statistical physics approach, since (i) physiologic systems often include many individual components, and (ii) physiologic systems usually are driven by competing forces, e.g., parasympathetic versus sympathetic stimuli. Physiologic systems often exhibit temporal structures which are similar to those found in physical systems driven away from an equilibrium state.
The missing link between cardiovascular rhythm control and myocardial cell modeling
Biomedizinische Technik/Biomedical Engineering, 2006
Cardiac arrhythmia is currently investigated from two different points of view. One considers ECG biosignal analysis and investigates heart rate variability, baroreflex control, heart rate turbulence, alternans phenomena, etc. The other involves building computer models of the heart based on ion channels, bidomain models and forward calculations to finally reach ECG and body surface potential maps. Both approaches aim to support the cardiologist in better understanding of arrhythmia, improving diagnosis and reliable risk stratification, and optimizing therapy. This article summarizes recent results and aims to trigger new research to bridge the different views.