100,370 research outputs found
Double symbolic joint entropy in nonlinear dynamic complexity analysis
Symbolizations, the base of symbolic dynamic analysis, are classified as
global static and local dynamic approaches which are combined by joint entropy
in our works for nonlinear dynamic complexity analysis. Two global static
methods, symbolic transformations of Wessel N. symbolic entropy and base-scale
entropy, and two local ones, namely symbolizations of permutation and
differential entropy, constitute four double symbolic joint entropies that have
accurate complexity detections in chaotic models, logistic and Henon map
series. In nonlinear dynamical analysis of different kinds of heart rate
variability, heartbeats of healthy young have higher complexity than those of
the healthy elderly, and congestive heart failure (CHF) patients are lowest in
heartbeats' joint entropy values. Each individual symbolic entropy is improved
by double symbolic joint entropy among which the combination of base-scale and
differential symbolizations have best complexity analysis. Test results prove
that double symbolic joint entropy is feasible in nonlinear dynamic complexity
analysis.Comment: 7 pages, 4 figure
Increment entropy as a measure of complexity for time series
Entropy has been a common index to quantify the complexity of time series in
a variety of fields. Here, we introduce increment entropy to measure the
complexity of time series in which each increment is mapped into a word of two
letters, one letter corresponding to direction and the other corresponding to
magnitude. The Shannon entropy of the words is termed as increment entropy
(IncrEn). Simulations on synthetic data and tests on epileptic EEG signals have
demonstrated its ability of detecting the abrupt change, regardless of
energetic (e.g. spikes or bursts) or structural changes. The computation of
IncrEn does not make any assumption on time series and it can be applicable to
arbitrary real-world data.Comment: 12pages,7figure,2 table
Nonlinear trend removal should be carefully performed in heart rate variability analysis
Background : In Heart rate variability analysis, the rate-rate time
series suffer often from aperiodic non-stationarity, presence of ectopic beats
etc. It would be hard to extract helpful information from the original signals.
10 Problem : Trend removal methods are commonly practiced to reduce
the influence of the low frequency and aperiodic non-stationary in RR data.
This can unfortunately affect the signal and make the analysis on detrended
data less appropriate. Objective : Investigate the detrending effect
(linear \& nonlinear) in temporal / nonliear analysis of heart rate variability
of long-term RR data (in normal sinus rhythm, atrial fibrillation, 15
congestive heart failure and ventricular premature arrhythmia conditions).
Methods : Temporal method : standard measure SDNN; Nonlinear methods
: multi-scale Fractal Dimension (FD), Detrended Fluctuation Analysis (DFA) \&
Sample Entropy (Sam-pEn) analysis. Results : The linear detrending
affects little the global characteristics of the RR data, either 20 in temporal
analysis or in nonlinear complexity analysis. After linear detrending, the
SDNNs are just slightly shifted and all distributions are well preserved. The
cross-scale complexity remained almost the same as the ones for original RR
data or correlated. Nonlinear detrending changed not only the SDNNs
distribution, but also the order among different types of RR data. After this
processing, the SDNN became indistinguishable be-25 tween SDNN for normal sinus
rhythm and ventricular premature beats. Different RR data has different
complexity signature. Nonlinear detrending made the all RR data to be similar ,
in terms of complexity. It is thus impossible to distinguish them. The FD
showed that nonlinearly detrended RR data has a dimension close to 2, the
exponent from DFA is close to zero and SampEn is larger than 1.5 -- these
complexity values are very close to those for 30 random signal.
Conclusions : Pre-processing by linear detrending can be performed on RR data,
which has little influence on the corresponding analysis. Nonlinear detrending
could be harmful and it is not advisable to use this type of pre-processing.
Exceptions do exist, but only combined with other appropriate techniques to
avoid complete change of the signal's intrinsic dynamics. 35 Keywords
heart rate variability linear / nonlinear detrending
complexity analysis mul-tiscale analysis detrended
fluctuation analysis fractal dimension sample entropy
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Gait variability: methods, modeling and meaning
The study of gait variability, the stride-to-stride fluctuations in walking, offers a complementary way of quantifying locomotion and its changes with aging and disease as well as a means of monitoring the effects of therapeutic interventions and rehabilitation. Previous work has suggested that measures of gait variability may be more closely related to falls, a serious consequence of many gait disorders, than are measures based on the mean values of other walking parameters. The Current JNER series presents nine reports on the results of recent investigations into gait variability. One novel method for collecting unconstrained, ambulatory data is reviewed, and a primer on analysis methods is presented along with a heuristic approach to summarizing variability measures. In addition, the first studies of gait variability in animal models of neurodegenerative disease are described, as is a mathematical model of human walking that characterizes certain complex (multifractal) features of the motor control's pattern generator. Another investigation demonstrates that, whereas both healthy older controls and patients with a higher-level gait disorder walk more slowly in reduced lighting, only the latter's stride variability increases. Studies of the effects of dual tasks suggest that the regulation of the stride-to-stride fluctuations in stride width and stride time may be influenced by attention loading and may require cognitive input. Finally, a report of gait variability in over 500 subjects, probably the largest study of this kind, suggests how step width variability may relate to fall risk. Together, these studies provide new insights into the factors that regulate the stride-to-stride fluctuations in walking and pave the way for expanded research into the control of gait and the practical application of measures of gait variability in the clinical setting
Maximum approximate entropy and r threshold: A new approach for regularity changes detection
Approximate entropy (ApEn) has been widely used as an estimator of regularity
in many scientific fields. It has proved to be a useful tool because of its
ability to distinguish different system's dynamics when there is only available
short-length noisy data. Incorrect parameter selection (embedding dimension
, threshold and data length ) and the presence of noise in the signal
can undermine the ApEn discrimination capacity. In this work we show that
() can also be used as a feature to
discern between dynamics. Moreover, the combined use of and
allows a better discrimination capacity to be accomplished, even in
the presence of noise. We conducted our studies using real physiological time
series and simulated signals corresponding to both low- and high-dimensional
systems. When is incapable of discerning between different
dynamics because of the noise presence, our results suggest that
provides additional information that can be useful for classification purposes.
Based on cross-validation tests, we conclude that, for short length noisy
signals, the joint use of and can significantly decrease
the misclassification rate of a linear classifier in comparison with their
isolated use
Nonlinear heart rate variability features for real-life stress detection. Case study : students under stress due to university examination
Background:
This study investigates the variations of Heart Rate Variability (HRV) due to a real-life stressor and proposes a classifier based on nonlinear features of HRV for automatic stress detection.
Methods:
42 students volunteered to participate to the study about HRV and stress. For each student, two recordings were performed: one during an on-going university examination, assumed as a real-life stressor, and one after holidays. Nonlinear analysis of HRV was performed by using Poincaré Plot, Approximate Entropy, Correlation dimension, Detrended Fluctuation Analysis, Recurrence Plot. For statistical comparison, we adopted the Wilcoxon Signed Rank test and for development of a classifier we adopted the Linear Discriminant Analysis (LDA).
Results:
Almost all HRV features measuring heart rate complexity were significantly decreased in the stress session. LDA generated a simple classifier based on the two Poincaré Plot parameters and Approximate Entropy, which enables stress detection with a total classification accuracy, a sensitivity and a specificity rate of 90%, 86%, and 95% respectively.
Conclusions:
The results of the current study suggest that nonlinear HRV analysis using short term ECG recording could be effective in automatically detecting real-life stress condition, such as a university examination
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