Reconstructing in-depth activity for chaotic 3D spatiotemporal excitable media models based on surface data.

Motivated by potential applications in cardiac research, we consider the task of reconstructing the dynamics within a spatiotemporal chaotic 3D excitable medium from partial observations at the surface. Three artificial neural network methods (a spatiotemporal convolutional long-short-term-memory, an autoencoder, and a diffusion model based on the U-Net architecture) are trained to predict the dynamics in deeper layers of a cube from observational data at the surface using data generated by the Barkley model on a 3D domain. The results show that despite the high-dimensional chaotic dynamics of this system, such cross-prediction is possible, but non-trivial and as expected, its quality decreases with increasing prediction depth.

Feedforward attractor targeting for non-linear oscillators using a dual-frequency driving technique.

A feedforward control technique is presented to steer a harmonically driven, non-linear system between attractors in the frequency-amplitude parameter plane of the excitation. The basis of the technique is the temporary addition of a second harmonic component to the driving. To illustrate this approach, it is applied to the Keller-Miksis equation describing the radial dynamics of a single spherical gas bubble placed in an infinite domain of liquid. This model is a second-order, non-linear ordinary differential equation, a non-linear oscillator. With a proper selection of the frequency ratio of the temporary dual-frequency driving and with the appropriate tuning of the excitation amplitudes, the trajectory of the system can be smoothly transformed between specific attractors; for instance, between period-3 and period-5 orbits. The transformation possibilities are discussed and summarized for attractors originating from the subharmonic resonances and the equilibrium state (absence of external driving) of the system.

Convolutional autoencoder and conditional random fields hybrid for predicting spatial-temporal chaos.

We present an approach for data-driven prediction of high-dimensional chaotic time series generated by spatially-extended systems. The algorithm employs a convolutional autoencoder for dimension reduction and feature extraction combined with a probabilistic prediction scheme operating in the feature space, which consists of a conditional random field. The future evolution of the spatially-extended system is predicted using a feedback loop and iterated predictions. The excellent performance of this method is illustrated and evaluated using Lorenz-96 systems and Kuramoto-Sivashinsky equations of different size generating time series of different dimensionality and complexity.

Changes of sleep-stage transitions due to ageing and sleep disorder.

Transition patterns between different sleep stages are analysed in terms of probability distributions of symbolic sequences for young and old subjects with and without sleep disorder. Changes of these patterns due to ageing are compared with variations of transition probabilities due to sleep disorder.

Synchronization based system identification of an extended excitable system.

A basic state and parameter estimation scheme for an extended excitable system is presented, where time series from a spatial grid of sampling points are used to drive and synchronize corresponding model equations. Model parameters are estimated by minimizing the synchronization error. This estimation scheme is demonstrated using data from generic models of excitable media exhibiting spiral wave dynamics and chaotic spiral break-up that are implemented on a graphics processing unit.

Bubble dynamics in a standing sound field: the bubble habitat.

Bubble dynamics is investigated numerically with special emphasis on the static pressure and the positional stability of the bubble in a standing sound field. The bubble habitat, made up of not dissolving, positionally and spherically stable bubbles, is calculated in the parameter space of the bubble radius at rest and sound pressure amplitude for different sound field frequencies, static pressures, and gas concentrations of the liquid. The bubble habitat grows with static pressure and shrinks with sound field frequency. The range of diffusionally stable bubble oscillations, found at positive slopes of the habitat-diffusion border, can be increased substantially with static pressure.

Modelling far field pacing for terminating spiral waves pinned to ischaemic heterogeneities in cardiac tissue.

In cardiac tissue, electrical spiral waves pinned to a heterogeneity can be unpinned (and eventually terminated) using electric far field pulses and recruiting the heterogeneity as a virtual electrode. While for isotropic media the process of unpinning is much better understood, the case of an anisotropic substrate with different conductivities in different directions still needs intensive investigation. To study the impact of anisotropy on the unpinning process, we present numerical simulations based on the bidomain formulation of the phase I of the Luo and Rudy action potential model modified due to the occurrence of acute myocardial ischaemia. Simulating a rotating spiral wave pinned to an ischaemic heterogeneity, we compare the success of sequences of far field pulses in the isotropic and the anisotropic case for spirals still in transient or in steady rotation states. Our results clearly indicate that the range of pacing parameters resulting in successful termination of pinned spiral waves is larger in anisotropic tissue than in an isotropic medium.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.

Detection and characterization of intermittent complexity variations in cardiac arrhythmia.

OBJECTIVE: A frequent observation during cardiac fibrillation is a fluctuation in complexity where the irregular pattern of the fibrillation is interrupted by more regular phases of varying length. APPROACH: We apply different measures to sliding windows of raw ECG signals for quantifying the temporal complexity. The methods include permutation entropy, power spectral entropy, a measure for the extent of the set of reconstructed states and several wavelet measures. MAIN RESULTS: Using these methods, variations of fibrillation patterns over time are detected and visualized. SIGNIFICANCE: These quantifications can be used to characterize different phases of the ECG during fibrillation and might improve diagnosis and treatment methods for heart diseases.

Estimability and dependency analysis of model parameters based on delay coordinates.

In data-driven system identification, values of parameters and not observed variables of a given model of a dynamical system are estimated from measured time series. We address the question of estimability and redundancy of parameters and variables, that is, whether unique results can be expected for the estimates or whether, for example, different combinations of parameter values would provide the same measured output. This question is answered by analyzing the null space of the linearized delay coordinates map. Examples with zero-dimensional, one-dimensional, and two-dimensional null spaces are presented employing the Hindmarsh-Rose model, the Colpitts oscillator, and the Rössler system.

Prediction of spatiotemporal time series based on reconstructed local states

Spatiotemporal time series are analyzed and predicted using reconstructed local states. As numerical examples the evolution of a Kuramoto-Sivashinsky equation and a coupled map lattice are predicted from previously sampled data.

Modeling parameter dependence from time series.

Two approaches for modeling of parameter dependence of dynamical systems from time series are investigated and applied to different examples. For both methods it is assumed that a few time series are available that have been measured for different (known) parameter values of the underlying (experimental) dynamical system. The objective is to model the changing dynamics of the system as a function of its parameters and to use this for experimental bifurcation analysis. Using parametrized families the tasks of modeling the dynamics and of modeling its parameter dependence are separated. Technical difficulties that may occur with this approach are discussed and illustrated. An alternative are extended state space models where both modeling tasks are treated simultaneously. To obtain reliable models from a few time series only, ensembles of models are employed that show very good extrapolation and generalization properties.

Synchronization using dynamic coupling.

A systematic coupling procedure is introduced for synchronizing arbitrary chaotic dynamical systems. This coupling exploits the existing contraction properties of the flow and suppresses divergence only along those directions in state space, where the underlying flow is not contracting. In this way, systems can be synchronized using a minimum of transmitted information for guaranteed high-quality synchronization. Applications in combination with sporadic driving and in partitioned state spaces are numerically illustrated.

Classifying cardiac biosignals using ordinal pattern statistics and symbolic dynamics.

The performance of (bio-)signal classification strongly depends on the choice of suitable features (also called parameters or biomarkers). In this article we evaluate the discriminative power of ordinal pattern statistics and symbolic dynamics in comparison with established heart rate variability parameters applied to beat-to-beat intervals. As an illustrative example we distinguish patients suffering from congestive heart failure from a (healthy) control group using beat-to-beat time series. We assess the discriminative power of individual features as well as pairs of features. These comparisons show that ordinal patterns sampled with an additional time lag are promising features for efficient classification.

Robust synchronization of chaotic systems.

The question of robustness of synchronization with respect to small arbitrary perturbations of the underlying dynamical systems is addressed. We present examples of chaos synchronization demonstrating that normal hyperbolicity is a necessary and sufficient condition for the synchronization manifold to be smooth and persistent under small perturbations. The same examples, however, show that in real applications normal hyperbolicity is not sufficient to give quantitative bounds for deformations of the synchronization manifold, i.e., even in the case of normal hyperbolicity two almost identical systems may cause large synchronization errors.

Synchronization and control of coupled ginzburg-landau equations using local coupling

In this paper we discuss the properties of a recently introduced coupling scheme for spatially extended systems based on local spatially averaged coupling signals [see Z. Tasev et al., Int. J. Bifurcation Chaos Appl. Sci. Eng. (to be published); and L. Junge et al., Int. J. Bifurcation Chaos Appl. Sci. Eng. 9, 2265 (1999)]. Using the Ginzburg-Landau model, we performed an extensive numerical examination of this coupling scheme, i.e., a complete scan through the relevant coupling parameters. Furthermore, we demonstrate suppression and control of spatiotemporal chaos, e.g., stabilizing the homogeneous steady state and spatially localized control. As an application all model parameters of the Ginzburg-Landau equation are estimated given only the local information of the system.

Phase synchronization of coupled ginzburg-landau equations

The occurrence of phase synchronization of a pair of unidirectionally coupled nonidentical Ginzburg-Landau equations is demonstrated and characterized using cyclic and extended phases. Furthermore, it is shown that weak coupling first leads to frequency synchronization and later to phase synchronization. For strong coupling there is evidence for generalized synchronization.