Titlebook: Nonlinear Analysis of Physiological Data; H. Kantz,J. Kurths,G. Mayer-Kress Conference proceedings 1998 Springer-Verlag Berlin Heidelberg

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H. Kantz,J. Kurths,G. Mayer-KressNonlinear methods to analyze predical data and their implementation in computer-aided diagnostics are very new but of growing importance despite their inherent complexity.

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Processing of Physiological DataIn the context of nonlinear time series analysis, preprocessing of data brings up two questions: how does the data processing step influence the performance of nonlinear algorithms, and how can ideas from nonlinear dynamics be used to improve the data? We will illustrate these issues with some exemplary physiological time series problems.

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Analysing Synchronization Phenomena from Bivariate Data by Means of the Hilbert TransformWe use the analytic signal approach based on the Hilbert transform to compute the phase difference between two non-stationary signals and find out epochs of phase locking.

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EEG Signal Analysis by Continuous Wavelet Transform TechniquesWavelet techniques are used to analyse EEG signals. In a first step the deviation of an expected power law determines the scale (frequency) at which some unexpected events happen. The positions of the local extrema of the wavelet transform are computed in a second step, this locates the instances at which these events take place.

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New Nonlinear Algorithms for Analysis of Heart Rate Variability: Low-Dimensional Chaos Predicts Lethstudy, several new algorithms based in deterministic chaos theory were applied to a data set made from 20 high-risk patients, each of whom had documented nonsustained ventricular tachycardia (VT) and 10 of whom manifested lethal ventricular fibrillation (VF) within 24 hr. Only the algorithms which m

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Classification of EEG Signals From a Music Perception Experiment Using Empirical Dynamical Modelsve classification scheme for the data. This scheme shows significant distinction between certain musical samples, and from this we infer several general conclusions about the sensitivity of the particular subject to the musical data classes.