Nonlinear time series analysis
Nonlinear time series analysis
Motor shaft misalignment detection using multiscale entropy with wavelet denoising
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Fast computation of sample entropy and approximate entropy in biomedicine
Computer Methods and Programs in Biomedicine
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Motivations: Physiological systems are ruled by mechanisms operating across multiple temporal scales. A recently proposed approach, multiscale entropy analysis, measures the complexity at different time scales and has been successfully applied to long term electrocardiographic recordings. The purpose of this work is to show the applicability of this methodology, rooted on statistical physics ideas, to short term time series of simultaneously acquired samples of heart rate, blood pressure and lung volume, from healthy subjects and from subjects with chronic heart failure. In the same spirit, we also propose a multiscale approach, to evaluate interactions between time series, by performing a multivariate autoregressive (AR) modeling of the coarse grained time series. Methods: We apply the multiscale entropy analysis to our data set of short term recordings. Concerning the multiscale version of the multivariate AR approach, we apply it to the four dimensional time series so as to detect scale dependent patterns of interactions between the physiological quantities. Results: Evaluating the complexity of signals at the multiple time scales inherent in physiologic dynamics, we find new quantitative indicators which are statistically correlated with the pathology. Our results show that multiscale entropy calculated on all the measured quantities significantly differs (P