I Directions in Nonlinear Data Analysis -- Processing of Physiological Data -- Problems in the Reconstruction of High-dimensional Deterministic Dynamics from Time Series -- Chaotic Measures and Real-World Systems: Does the Lyapunov Exponent Always Measure Chaos -- Ranking and Entropy Estimation in Nonlinear Time Series Analysis -- Analysing Synchronization Phenomena from Bivariate Data by Means of the Hilbert Transform -- Analyzing Spatio-Temporal Patterns of Complex Systems -- II Cardio-Respiratory System -- Are R-R-Interval Data Appropriate to Study the Dynamics of the Heart -- New Nonlinear Algorithms for Analysis of Heart Rate Variability: Low-Dimensional Chaos Predicts Lethal Arrhythmias -- Nonlinear Analysis of the Cardiorespiratory Coordination in a Newborn Piglet -- Cardiorespiratory Synchronization -- III EEG Analysis -- EEG Signal Analysis by Continuous Wavelet Transform Techniques -- Relativity of Dynamical Measures in Study of the Human Brain: Local Lyapunov Exponent as a Tool for Comparative Studies -- Classification of EEG Signals Prom a Music Perception Experiment Using Empirical Dynamical Models -- IV Perception and Motor Control -- Nonlinear Analysis of Perceptual-Motor Coupling in the Developement of Postural Control -- Symbolic Dynamics of Bimanual Production of Polyrhythms -- Human Postural Control: Force Plate Experiments and Modelling -- Delay Induced Patterns of Visually Guided Movements -- V Voice -- Detecting Bifurcations in Voice Signals.
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SUMMARY OR ABSTRACT
Text of Note
This book surveys recent developments in the analysis of physiological time series. The authors, physicists and mathematicans, physiologists and medical researchers, have succeeded in presenting a review of the new field of nonlinear data analysis as needed for more refined computer-aided diagnostics. Together with the techniques, they actually propose a new approach to the problems. Practitioners may find the many applications to the cardio-respiratory system, EEG analysis, motor control and voice signals very useful.