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Knowledge of a cause-and-effect relationship between different physiological systems is helpful in predicting their performance under perturbations, such as orthostatic challenge. The causal coupling between representative signals of the cardiovascular and postural systems under orthostatic challenge remains unknown. Understanding the causal relationship between these two systems is critical, as their...
We apply the sublinear time, scalable locality-sensitive hashing (LSH) and majority discrimination to the problem of predicting critical events based on physiological waveform time series. Compared to using the linear exhaustive k-nearest neighbor search, our proposed method vastly speeds up prediction time up to 25 times while sacrificing only 1% of accuracy when demonstrated on an arterial blood...
For the assessment of autonomic nervous system activity based on heart rate variability (HRV) analysis, characteristics of high-frequency (HF; 0.15 to 0.4 Hz) and low-frequency (LF; 0.04 to 0.15 Hz) components have been widely employed. HF and LF band powers quantified by power spectral analysis have most commonly been used in the conventional studies; the physiological significance of these measures...
Complexity of RR intervals can be measured by Sample entropy (SampEn) traditionally estimated only for a limited number of points by fixing the values of the embedding dimension "m" and distance threshold "r". Recently we have proposed a new and fast Norm Component Matrix (NCM) algorithm for SampEn calculation — it allows analyzing whole ranges of (m, r) values leading to entropy...
During the last years a lot of papers have appeared dealing with the applications to physiological time series of some parameters originally found in Dynamical Systems. In this work, we looked for the Largest Lyapunov Exponents (LLE) in heart rate time series of healthy men in order to verify if it was possible to find the Anaerobic Threshold (AT) in a non-invasive way using just the time series and...
Sample entropy (SampEn) is a popular complexity measure in HRV analysis. SampEn is estimated by fixing the values of the embedding dimension "m" and distance threshold "r" and traditionally SampEn is calculated with m=2 and r=0.2 times the standard deviation of the series. Attempts to extend the estimates to different (m, r) pairs are hampered by the high computational burden of...
Testing for nonlinearity is one of the most important preprocessing steps in nonlinear time series analysis. Typically, this is done by means of the linear surrogate data methods. But it is a known fact that the validity of the results heavily depends on the stationarity of the time series. Since most physiological signals are non-stationary, it is easy to falsely detect nonlinearity using the linear...
The scientific and clinical value of a measure of complexity is potentially enormous because complexity appears to be lost in the presence of illness. The changes introduced by asthma in respiratory mechanics and control of breathing may result in modifications in the airflow pattern. These changes may be interesting clinically, since they can reduce the ability of the patient to perform daily life...
We discuss the merits of adaptive statistical models for biosignals in a daily life context. Processing of this type of signals poses a number of challenges. First, it is clear that an adaptive model is needed to tailor for the differences in physiology between individuals, as well as adapt to someone's current physiological state. Second, in a daily life setting we use unobtrusive measurement devices,...
We proposed and developed a novel algorithm, named multiscale cross entropy (MSCE), to assess the dynamical characteristics of coupling behavior between two sequences on multiple scales, and apply it into the analysis of ??coupling behavior?? between two variables in physical and physiological systems, such as Henon-Henon map, Ro??ssler-Lorenz differential equations and autonomic nervous system. The...
In recent decades many research effort has been expended in the field of noninvasive, continuous blood pressure (BP) estimation by cardiovascular surrogate parameters, mainly the pulse transit time (PTT). Due to differences in the measurement setup and in the consideration of important physiological aspects, however, there is a multitude of inconsistent statements about the BP tracking capabilities...
An efficient nonlinear analysis method is proposed to characterize the dynamics of physiological time series. This method consists of analyzing the symbolic dynamics of the reconstructed phase space of a time series. Since a physiological time series is usually nonstationary, to compensate for the time varying local mean and extract the wave characteristics of the time series, all the vectors in the...
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