Objectives: To determine how different mathematical time series approaches can be implemented for the detection of qualitative patterns in physiologic monitoring data, and which of these approaches could be suitable as a basis for future bedside time series analysis. Interventions: None. Measurements and results:Hemodynamic data were acquired in 1-minute intervals from a clinical information system and exported into statistical software for further analysis. Altogether, 134 time series for heart rate, mean arterial pressure and mean pulmonary artery pressure were visually classified by a senior intensivist into five patterns: no change, outlier, temporary level change, permanent level change, and trend. The same series were analyzed with low order autoregressive (AR) models and with phase space (PS) models. The resulting classifications from both models were compared to the initial classification. Outliers and level changes were detected in most instances with both methods. Trend detection could only be done indirectly. Both methods were more sensitive to pattern changes than they were clinically relevant. Especially with outlier detection, 95% confidence intervals were too close. AR models require direct user interaction, whereas PS models offer opportunities for fully automated time series analysis in this context. Conclusion:Statistical patterns in univariate intensive care time series can reliably be detected with AR models and with PS models. For most bedside problems both methods are too sensitive. AR models are highly interactive, and both methods require that users have an explicit knowledge of statistics. While AR models and PS models can be extremely useful in the scientific off-line analysis, routine bedside clinical use cannot yet be recommended.

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Unstructured grids greatly ease the mesh generation process in the case of complex geometries. The Discontinuous Galerkin Method (DGM) provides a robust, high-order accurate discretization even on this type of grid. The goal of the work reported herein is the prediction of broadband airframe noise generated by an airfoil with a deployed slat. Focus is on the noise generated by the slat, and two spatial dimensions are considered as a first step. The particularly employed DGM employs Lagrange polynomials as shape functions. They enable a simple and cheap truncation of the flux quantities of the underlying Acoustic Perturbation Equations (APE). The method is tested by computing the sound field of a monopole placed in a laminar boundary layer. Computations are stable, and very good agreement with other computations and with theoretical results is observed. Considering the prediction of broadband slat noise, the turbulent source term of the APE is computed efficiently via the stochastic FRPM (Fast Random Particle Mesh) method. Very encouraging results are obtained, and these will be analyzed in the next step.

Abstract. As high dimensional data occur as a rule rather than an exception in critical care today, it is of utmost importance to improve acquisition, storage, modelling, and analysis of medical data, which appears feasable only with the help of bedside computers. The use of clinical information systems o ers new perspectives of data recording and also causes a new challenge for statistical methodology. A graphical approach for analysing patterns in statistical time series from online monitoring systems in intensive care is proposed here as an example of a simple univariate method, which contains the possibility of a multivariate extension and which can be combined with procedures for dimension reduction.

A promising way to predict airframe noise is the numerical solution of the Acoustic Perturbation Equations (APE) with the help of the Discontinuous Galerkin Method (DGM) and the Fast Random Particle Mesh (FRPM) method. The latter stochastically computes the turbulent source term of the APE, while the DGM reliably provides their spatial discretization, even on a flexible unstructured grid. The goal of the current work is to compute broadband slat noise of two different high-lift airfoil configurations in two dimensions with this approach. The dependency of the result on the resolution of both the FRPM-as well as the DG-grid is analysed, and the parameter sensitivity of the FRPM method is investigated. Furthermore, computed sound pressure spectra are compared to measured spectra, and computational times are examined.