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Introduction: Acute ischemic stroke is one of the leading causes of disability globally, requiring the best-integrated approach between prevention, intervention, and therapies aiming to avoid the worse outcome scenario. A careful analysis of potential clinical predictors after stroke under the current data science paradigms can significantly improve prognosis reliability and lead to the anticipation of the most suitable rehabilitation program to be adopted, with crucial benefits to avoid severe disabilities. Among the predictors used, the Alberta Stroke Program Early CT Score (ASPECTS) deserves special attention, given its standardized assessment by evaluating computed tomography (CT) scans. Despite its widespread use, the ASPECTS has some limitations since it does not consider different eloquent brain regions impact on stroke patients’ functional outcome or even the lesion severity of the respective area. Objective: This study aims to identify the eloquent brain areas most related to the patients’ prognosis through machine learning techniques, considering a binary (affected or non-affected region — as ranked under ASPECTS paradigm) and a weighted (3-level damage severity degree) approach. The stroke outcome prediction performance was investigated and compared with results obtained under the classical ASPECTS. Methods: We applied a wrapper feature selection based on sequential forward strategy combined with a linear discriminant analysis classifier considering: (1) the binary lesion approach for investigating the role of critical eloquent brain areas; (2) the weighted lesion approach for investigating the role of lesion severity; (3) the ASPECTS predictor performance for benchmark comparison. Results: We identified six promising areas and observed that the weighted approach had the best performance among the binary option and ASPECTS scenarios. Conclusion: Despite being more complex, critical eloquent brain areas and damage severity can play a crucial role in improving clinical prediction in ischemic stroke prognosis, which deserves careful attention in the context of the current data science techniques.

Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers.

This work has a twofold aim: (a) to analyze an alternative approach for computing the conditional Lyapunov exponent ( λ cmax ) aiming to evaluate the synchronization stability between nonlinear oscillators without solving the classical variational equations for the synchronization error dynamical system. In this first framework, an analytic reference value for λ cmax is also provided in the context of Duffing master-slave scenario and precisely evaluated by the proposed numerical approach; (b) to apply this technique to the study of synchronization stability in chaotic Hindmarsh-Rose ( HR ) neuronal models under uni- and bi-directional resistive coupling and different excitation bias, which also considered the root mean square synchronization error, information theoretic measures and asymmetric transfer entropy in order to offer a better insight of the synchronization phenomenon. In particular, statistical and information theoretical measures were able to capture similarity increase between the neuronal oscillators just after a critical coupling value in accordance to the largest conditional Lyapunov exponent behavior. On the other hand, transfer entropy was able to detect neuronal emitter influence even in a weak coupling condition, i.e. under the increase of conditional Lyapunov exponent and apparently desynchronization tendency. In the performed set of numerical simulations, the synchronization measures were also evaluated for a two-dimensional parameter space defined by the neuronal coupling (emitter to a receiver neuron) and the (receiver) excitation current. Such analysis is repeated for different feedback couplings as well for different (emitter) excitation currents, revealing interesting characteristics of the attained synchronization region and conditions that facilitate the emergence of the synchronous behavior. These results provide a more detailed numerical insight of the underlying behavior of a HR in the excitation and coupling space, being in accordance with some general findings concerning HR coupling topologies. As a perspective, besides the synchronization overview from different standpoints, we hope that the proposed numerical approach for conditional Lyapunov exponent evaluation could outline a valuable strategy for studying neuronal stability, especially when realistic models are considered, in which analytical or even Jacobian evaluation could define a laborious or impracticable task. [ABSTRACT FROM AUTHOR]

The characterization of nonlinear dynamical systems and their attractors in terms of invariant measures, basins of attractions and the structure of their vector fields usually outlines a task strongly related to the underlying computational cost. In this work, the practical aspects related to the use of parallel computing – specially the use of Graphics Processing Units ( GPUs ) and of the Compute Unified Device Architecture ( CUDA ) – are reviewed and discussed in the context of nonlinear dynamical systems characterization. In this work such characterization is performed by obtaining both local and global Lyapunov exponents for the classical forced Duffing oscillator. The local divergence measure was employed by the computation of the Lagrangian Coherent Structures ( LCSs ), revealing the general organization of the flow according to the obtained separatrices, while the global Lyapunov exponents were used to characterize the attractors obtained under one or more bifurcation parameters. These simulation sets also illustrate the required computation time and speedup gains provided by different parallel computing strategies, justifying the employment and the relevance of GPUs and CUDA in such extensive numerical approach. Finally, more than simply providing an overview supported by a representative set of simulations, this work also aims to be a unified introduction to the use of the mentioned parallel computing tools in the context of nonlinear dynamical systems, providing codes and examples to be executed in MATLAB and using the CUDA environment, something that is usually fragmented in different scientific communities and restricted to specialists on parallel computing strategies. [ABSTRACT FROM AUTHOR]

Abstract: Classically, adaptive equalization algorithms are analyzed in terms of two possible steady state behaviors: convergence to a fixed point and divergence to infinity. This twofold scenario suits well the modus operandi of linear supervised algorithms, but can be rather restrictive when unsupervised methods are considered, as their intrinsic use of higher-order statistics gives rise to nonlinear update expressions. In this work, we show, using different analytical tools belonging to dynamic system theory, that one of the most emblematic and studied unsupervised approaches – the decision-directed algorithm – is potentially capable of presenting behaviors, like convergence to limit-cycles and chaos, that transcend the aforementioned dichotomy. These results also indicate theoretical possibilities concerning step-size selection and initialization. [Copyright &y& Elsevier]

Abstract: This work aims to present a new method to perform blind extraction of chaotic deterministic sources mixed with stochastic signals. This technique employs the recurrence quantification analysis (RQA), a tool commonly used to study dynamical systems, to obtain the separating system that recovers the deterministic source. The method is applied to invertible and underdetermined mixture models considering different stochastic sources and different RQA measures. A brief discussion about the influence of recurrence plot parameters on the robustness of the proposal is also provided and illustrated by a set of representative simulations. [Copyright &y& Elsevier]