Analysis of the Nonlinear Autodependencies of Respiratory Pattern Variability in Patients on Weaning Trials

Traditional time domain techniques of data analysis are often not sufficient to characterize the nonlinear dynamics of respiration. In this study, the respiratory pattern variability was analyzed using auto mutual information measures. These provide access to nonlinear statistical autodependencies of respiratory pattern variability. A group of 20 patients on weaning trials from mechanical ventilation were studied at two different pressure support ventilation levels, in order to obtain respiratory volume signals with different variability. Time series of breathing duration, inspiratory time, fractional inspiratory time, tidal volume and mean inspiratory flow were analyzed. Different measures based on Auto-Mutual Information were studied to characterize the respiratory pattern variability with regard to its complex organization. 1 The traditional of data analysis in the time and frequency domains are often not sufficient to characterize the complex dynamics of respiration. Various attempts have been reported to apply the concept of nonlinear dynamics to the analysis of complex physiological systems. Several methods describing nonlinear dynamics and statistics underlying the variability of physiological time series have been proposed: Correlation dimension, Lyapunov exponents, Shannon entropy, nonlinear prediction, etc (11-14). Some of these approaches may present limitations according to the length of the time series and can even lead to misinterpretations of data. In this work, we introduce nonlinear analyses of respiratory dynamics that may enable the underlying physiological processes. The object of the investigation is the quantitative analysis of the nonlinear behavior of the respiratory dynamics with regard to its complex organization. This analysis could be of importance for the automatic classification of the volume signals in high or low variability. In this way, we apply Mutual Information by means of Auto Mutual Information Function (AMIF). We propose different AMIF measures and to analyze their discriminatory impact with regard to different respiratory pattern variability.

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