Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

Finite-time transport in volume-preserving flows

Finite-time transport between distinct flow regions is of great relevance to many scientific applications, yet quantitative studies remain scarce to date. The primary obstacle is computing the evolution of material volumes, which is often infeasible due to extreme interfacial stretching. We present a framework for describing and computing nite-time transport in n-dimensional (chaotic) volume-preserving flows that relies on the reduced dynamics of an (n - 2)-dimensional minimal set of fundamental trajectories. This approach has essential advantages over existing methods: the regions between which transport is investigated can be arbitrarily specified; no knowledge of the flow outside the nite transport interval is needed; and computational effort is substantially reduced. We demonstrate our framework in 2D for an industrial mixing device

On some properties of three-dimensional mixing systems

This article develops some general observations on the statistical, geometrical and dynamic properties of three-dimensional autonomous and periodically forced mixing systems. The main geometrical differences between the two-dimensional and three-dimensional cases, and between autonomous and time-periodic velocity fields are discussed in detail. Although the article makes use exclusively of model systems of three-dimensional flows, the results obtained give useful hints to approach a global characterization of mixing in types of industrial equipment such as stirred vessels